Fluid analysis and monitoring using optical spectroscopy

ABSTRACT

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.16/000,616, filed Jun. 5, 2018, which is a continuation of U.S. patentapplication Ser. No. 15/997,612, filed Jun. 4, 2018, which claims thebenefit of U.S. Provisional Patent Application No. 62/598,912, filedDec. 14, 2017, U.S. Provisional Patent Application No. 62/596,708, filedDec. 8, 2017, U.S. Provisional Patent Application No. 62/569,384, filedOct. 6, 2017, and U.S. Provisional Patent Application No. 62/514,572,filed Jun. 2, 2017. U.S. patent application Ser. No. 15/997,612 is alsoa continuation-in-part of U.S. patent application Ser. No. 15/139,771,filed Apr. 27, 2016, which claims the benefit of U.S. Provisional PatentApplication No. 62/237,694, filed Oct. 6, 2015, U.S. Provisional PatentApplication No. 62/205,315, filed Aug. 14, 2015, and U.S. ProvisionalPatent Application No. 62/153,263, filed Apr. 27, 2015. The contents ofthe above-referenced patent applications are incorporated herein byreference in their entireties.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The accompanying drawings form a part of the disclosure and areincorporated into the subject specification. The drawings illustrateexample embodiments and, in conjunction with the specification andclaims, serve to explain various principles, features, or aspects of thedisclosure. Certain embodiments are described more fully below withreference to the accompanying drawings. However, various aspects beimplemented in many different forms and should not be construed aslimited to the implementations set forth herein. Like numbers refer tolike, but not necessarily the same or identical, elements throughout.

FIG. 1 is a schematic of a fluid analysis and monitoring system,according to an example embodiment of the present disclosure.

FIG. 2 is a schematic of a spectroscopy system connected to a fluidsource, according to an example embodiment of the present disclosure.

FIG. 3 is a schematic of a spectroscopy system connected to a fluidsource, according to an example embodiment of the present disclosure.

FIG. 4 is a schematic of a spectroscopy system connected to a fluidsource, according to an example embodiment of the present disclosure.

FIG. 5 is a schematic of a spectroscopy system connected to fluidsources, according to an example embodiment of the present disclosure.

FIG. 6 shows a sample chamber with sensors, according to an exampleembodiment of the present disclosure.

FIG. 7 shows an enlarged cross-sectional view of the T-shaped opticalsampling chamber shown in FIG. 6, according to an example embodiment ofthe present disclosure.

FIG. 8A is an isometric view of a sample chamber, according to anexample embodiment of the present disclosure.

FIG. 8B is a top view of the sample chamber of FIG. 8A, according to anexample embodiment of the present disclosure.

FIG. 8C is a partially exploded side view of the sample chamber of FIG.8A, according to an example embodiment of the present disclosure.

FIG. 8D shows a portion of a sampling chamber with ports for an opticalprobe and a viscometer, according to an example embodiment of thepresent disclosure.

FIG. 9A shows an optical probe connected to a portion of a T-shapedoptical sampling chamber shown in FIG. 6, according to an exampleembodiment of the present disclosure.

FIG. 9B shows a partial cross-sectional view of a straight-line samplingchamber connected to an optical probe, according to an exampleembodiment of the present disclosure.

FIG. 10 shows an optical probe and cover, according to an exampleembodiment of the present disclosure.

FIG. 11A is a partial cross-sectional view of a fluid source with animmersion probe and a viscometer coupled to the fluid source, accordingto an example embodiment of the present disclosure.

FIG. 11B shows partial cross-sectional views of two fluid sources withimmersion probes connected to each fluid source, according to an exampleembodiment of the present disclosure.

FIG. 12 is a schematic of a fluid analysis system, according to anexample embodiment of the present disclosure.

FIG. 13A shows a fluid analysis system, according to an exampleembodiment of the present disclosure.

FIG. 13B shows a fluid analysis system, according to an exampleembodiment of the present disclosure.

FIG. 14 shows a nano chip plug of the fluid analysis system of FIG. 13A,according to an example embodiment of the present disclosure.

FIG. 15A shows a fluid analysis system, according to an exampleembodiment of the present disclosure.

FIG. 15B shows a node that may be used with the fluid analysis system ofFIG. 15A, according to an example embodiment of the present disclosure.

FIG. 16 is a schematic of a fluid analysis and monitoring system,according to an example embodiment of the present disclosure.

FIG. 17 is a schematic of a fluid analysis system, according to anexample embodiment of the present disclosure.

FIG. 18 is a schematic of a fluid analysis system, according to anexample embodiment of the present disclosure.

FIG. 19 is a schematic of a fluid analysis system with an enclosure anda cooling system, according to an example embodiment of the presentdisclosure.

FIG. 20 is a schematic of a fluid analysis system with an enclosure anda cooling system, according to an example embodiment of the presentdisclosure.

FIG. 21 is a schematic of a fluid analysis system with an enclosure anda cooling system, according to an example embodiment of the presentdisclosure.

FIG. 22 is a schematic of a fluid analysis system with an enclosure anda cooling system, according to an example embodiment of the presentdisclosure.

FIG. 23 is a schematic of a sampling system, according to an exampleembodiment of the present disclosure.

FIG. 24 is a schematic of a sub-sampling system that may be used in thesampling system of FIG. 23, according to an example embodiment of thepresent disclosure.

FIG. 25A is a schematic of a Raman sub-sampling system that may be usedwith the sampling system of FIG. 23, according to an example embodimentof the present disclosure.

FIG. 25B is a partial cross-sectional view of a Raman probe that may beused in the Raman sub-sampling system of FIG. 25A, according to anexample embodiment of the present disclosure.

FIG. 26A is a schematic of a fluorescence sub-sampling system that maybe used with the sampling system of FIG. 23, according to an exampleembodiment of the present disclosure.

FIG. 26B shows a reflection probe that may be used in the fluorescencesub-sampling system of FIG. 26A, according to an example embodiment ofthe present disclosure.

FIG. 27A is a schematic of an absorbance sub-sampling system that may beused with the sampling system of FIG. 23, according to an exampleembodiment of the present disclosure.

FIG. 27B shows a transmission dip probe that may be used in theabsorbance sub-sampling system of FIG. 27A, according to an exampleembodiment of the present disclosure.

FIG. 28A is a schematic of a Fourier Transform Infra-Red (FTIR)absorbance sub-sampling system that may be used with the sampling systemof FIG. 23, according to an example embodiment of the presentdisclosure.

FIG. 28B is an illustration of an FTIR process performed by the FTIRabsorbance sub-sampling system of FIG. 28A, according to an exampleembodiment of the present disclosure.

FIG. 29 is a schematic of an absorbance/fluorescence/scattersub-sampling system that may be used with the sampling system of FIG.23, according to an example embodiment of the present disclosure.

FIG. 30A is a schematic of a multi-source fluid sampling system,according to an example embodiment of the present disclosure.

FIG. 30B is a schematic of a fluid sampling system, according to anexample embodiment of the present disclosure.

FIG. 30C is a schematic of a cooling system, according to an exampleembodiment of the present disclosure.

FIG. 31A is a flowchart illustrating a method of operating a fluidanalysis system, according to an example embodiment of the presentdisclosure.

FIG. 31B is a continuation of the flow chart of FIG. 31A, according toan example embodiment of the present disclosure.

FIG. 32 is a flowchart illustrating a method of operating an analyticalsystem, according to an example embodiment of the present disclosure.

FIG. 33 is a flowchart illustrating a method of operating analyticalsystems to implement a power calibration for Raman sub-sampling systemof FIG. 25A, according to an example embodiment of the presentdisclosure.

FIG. 34 is a flowchart illustrating a method of measuring and monitoringviscosity, according to an example embodiment of the present disclosure.

FIG. 35A illustrates Raman spectroscopy data for a first concentrationof soot in motor oil, according to an example embodiment of the presentdisclosure.

FIG. 35B illustrates Raman spectroscopy data for a second concentrationof soot in motor oil, according to an example embodiment of the presentdisclosure.

FIG. 35C illustrates Raman spectroscopy data for a third concentrationof soot in motor oil, according to an example embodiment of the presentdisclosure.

FIG. 36A illustrates the data of FIG. 35B after it has beenpre-processed, according to an example embodiment of the presentdisclosure.

FIG. 36B shows the data of FIG. 35C after it has been pre-processed,according to an example embodiment of the present disclosure.

FIG. 36C is a data plot of a mathematical approximation to the Ramanspectroscopic features associated with soot, according to an exampleembodiment of the present disclosure.

FIG. 37 illustrates a mathematical function that characterizesoverlapping spectral peaks, according to an example embodiment of thepresent disclosure.

FIG. 38 illustrates a complicated spectrum having multiple overlappingpeaks along with an expanded view of a portion of the spectrum,according to an example embodiment of the present disclosure.

FIG. 39 illustrates a plurality of frequency windows to define areasunder the curve of FIG. 38, according to an example embodiment of thepresent disclosure.

FIG. 40 is a table of feature area values each corresponding torespective frequency values according to an example embodiment of thepresent disclosure.

FIG. 41 is a table of feature area values vs. frequency for a pluralityof systems, according to an example embodiment of the presentdisclosure.

FIG. 42 is a data plot of computed areas vs. frequency illustratingminima that may be used to identify spectral features, according to anexample embodiment of the present disclosure.

FIG. 43A is data plot of count values vs. frequency values for four databuckets corresponding to four respective ranges of concentrations ofiron-based impurities in motor oil, according to an example embodimentof the present disclosure.

FIG. 43B is a data plot showing count values vs. frequency values foronly the low concentration buckets of FIG. 43A, according to an exampleembodiment of the present disclosure.

FIG. 43C is a data plot showing count values vs. frequency values forhigh concentration buckets of FIG. 43A, according to an exampleembodiment of the present disclosure.

FIG. 44A is a data plot of count values generated by excluding countvalues that fall below the threshold line of the data plot of 43A,according to an example embodiment of the present disclosure.

FIG. 44B illustrates shaded regions indicating frequency windowsassociated with the peaks of FIG. 44A, according to an exampleembodiment of the present disclosure.

FIG. 44C is a data plot of a numerical representation of a series ofGaussian functions, each centered on a corresponding frequency window,according to an example embodiment of the present disclosure.

FIG. 44D is a bar chart indicating a value for a sum of areas of peaksin each frequency window of FIG. 44A, according to an example embodimentof the present disclosure.

FIG. 45 is an illustration of data characterized by a two-dimensionalclassifier model, according to an example embodiment of the presentdisclosure.

FIG. 46 is a data plot of Raman spectral data of pure ethylene glycolaccording to an example embodiment of the present disclosure.

FIG. 47A is a data plot of count values vs. frequency for lowconcentrations of coolant in motor oil, obtained using a first laserthat generates incident radiation of wavelength of 680 nm, according toan example embodiment of the present disclosure.

FIG. 47B is a data plot of count values vs. frequency for lowconcentrations of coolant in motor oil, obtained using a second laserthat generates incident radiation of wavelength of 785 nm, according toan example embodiment of the present disclosure.

FIG. 47C is a data plot of count values vs. frequency for mediumconcentrations of coolant in motor oil, obtained using a first laserthat generates incident radiation of wavelength of 680 nm, according toan example embodiment of the present disclosure.

FIG. 47D is a data plot of count values vs. frequency for mediumconcentrations of coolant in motor oil, obtained using a second laserthat generates incident radiation of wavelength of 785 nm, according toan example embodiment of the present disclosure.

FIG. 47E is a data plot of count values vs. frequency for highconcentrations of coolant in motor oil, obtained using a first laserthat generates incident radiation of wavelength of 680 nm, according toan example embodiment of the present disclosure.

FIG. 47F is a data plot of count values vs. frequency for highconcentrations of coolant in motor oil, obtained using a second laserthat generates incident radiation of wavelength of 785 nm, according toan example embodiment of the present disclosure.

FIG. 48A is a box plot that illustrates a distribution of sums of peakareas for important frequency windows for coolant in motor oil,according to an example embodiment of the present disclosure.

FIG. 48B is a violin plot showing the distribution of sums of peak areasof FIG. 48A, according to an example embodiment of the presentdisclosure.

FIG. 49 plots the data of FIGS. 48A and 48B projected onto the varioustwo-dimensional planes so that the distribution of area sums for low andhigh concentrations of coolant in motor oil may be investigatedvisually, according to an example embodiment of the present disclosure.

FIG. 50 illustrates results obtained from a Support Vector Machine modelof coolant in motor oil, according to an example embodiment of thepresent disclosure.

FIG. 51A is a data plot of count values vs. frequency for lowconcentrations of fuel in motor oil, obtained using a first laser thatgenerates incident radiation of wavelength of 680 nm, according to anexample embodiment of the present disclosure.

FIG. 51B is a data plot of count values vs. frequency for lowconcentrations of fuel in motor oil, obtained using a second laser thatgenerates incident radiation of wavelength of 785 nm, according to anexample embodiment of the present disclosure.

FIG. 51C is a data plot of count values vs. frequency for mediumconcentrations of fuel in motor oil, obtained using a first laser thatgenerates incident radiation of wavelength of 680 nm, according to anexample embodiment of the present disclosure.

FIG. 51D is a data plot of count values vs. frequency for mediumconcentrations of fuel in motor oil, obtained using a second laser thatgenerates incident radiation of wavelength of 785 nm, according to anexample embodiment of the present disclosure.

FIG. 51E is a data plot of count values vs. frequency for highconcentrations of fuel in motor oil, obtained using a first laser thatgenerates incident radiation of wavelength of 680 nm, according to anexample embodiment of the present disclosure.

FIG. 51F is a data plot of count values vs. frequency for highconcentrations of fuel in motor oil, obtained using a second laser thatgenerates incident radiation of wavelength of 785 nm, according to anexample embodiment of the present disclosure.

FIG. 52A is a box plot that illustrates a distribution of sums of peakareas for important frequency windows for fuel in motor oil, accordingto an example embodiment of the present disclosure.

FIG. 52B is a violin plot showing the distribution of sums of peak areasof FIG. 52A, according to an example embodiment of the presentdisclosure.

FIG. 53 plots the data of FIGS. 52A and 52B projected onto the varioustwo-dimensional planes so that the distribution of area sums for low andhigh concentrations of fuel in motor oil may be investigated visually,according to an example embodiment of the present disclosure.

FIG. 54 illustrates results obtained from a Support Vector Machine modelof fuel in motor oil, according to an example embodiment of the presentdisclosure.

FIG. 55A is a data plot of count values vs. frequency for lowconcentrations of soot in motor oil, obtained using a first laser thatgenerates incident radiation of wavelength of 680 nm, according to anexample embodiment of the present disclosure.

FIG. 55B is a data plot of count values vs. frequency for highconcentrations of soot in motor oil, obtained using the first laser thatgenerates incident radiation of wavelength of 680 nm, according to anexample embodiment of the present disclosure.

FIG. 55C is a data plot of count values vs. frequency for lowconcentrations of soot in motor oil, obtained using a second laser thatgenerates incident radiation of wavelength of 785 nm, according to anexample embodiment of the present disclosure.

FIG. 55D is a data plot of count values vs. frequency for highconcentrations of soot in motor oil, obtained using the second laserthat generates incident radiation of wavelength of 785 nm, according toan example embodiment of the present disclosure.

FIG. 56 data plots the data of FIGS. 55A to 55D projected onto thevarious two-dimensional planes so that the distribution of area sums forlow and high concentrations of fuel in motor oil may be investigatedvisually, according to an example embodiment of the present disclosure.

FIG. 57 is a box plot that illustrates a distribution of sums of peakareas for important frequency windows for soot in motor oil, accordingto an example embodiment of the present disclosure.

FIG. 58 illustrates results obtained from a decision tree model of sootin motor oil, according to an example embodiment of the presentdisclosure.

FIG. 59 is a flowchart that summarizes data analysis methods employedherein, according to an example embodiment of the present disclosure.

FIG. 60 is a data plot of viscosity vs. fuel content in oil, accordingto an example embodiment of the present disclosure.

DETAILED DESCRIPTION

One of the keys to keeping machinery operating at optimal performance ismonitoring and analyzing working fluids, including lubricant oils, forcharacteristics such as contamination, chemical content, and viscosity.The existence or amount of debris and particles from wearing parts,erosion, and contamination provide insights about issues affectingperformance and reliability. Indeed, accurately and effectivelyanalyzing and trending data about a fluid may be critical to theperformance and reliability of a particular piece of equipment. Thebenefits of improved predictive monitoring and analysis of fluidsinclude: optimized machinery performance, optimized maintenance planningand implementation, lower operational and maintenance costs, feweroutages, improved safety, and improved environmental impacts.

The present disclosure provides improved systems and methods for fluidmonitoring and analysis. Disclosed systems and methods accurately andeffectively gather, trend and analyze key data for improved proactivepredictive maintenance. Embodiments of the present disclosure includeautomated systems that directly monitor multiple conditions of a fluid,for example, engine oil actively flowing through working engines. Inembodiments, a single system is provided that actively monitors thecondition of fluids flowing through multiple pieces of machinery, forexample, oils flowing through multiple engines, on a set schedule oron-demand as directed by an operator using a web-based portal or amobile application. Fluids may be analyzed while machinery is on-linesuch that normal operation is not disrupted. Fluids can be effectivelymonitored and analyzed real-time, that is, a report can be sent to anoperator in minutes. This is a significant improvement over conventionaloil analysis systems, which may involve collecting a sample from aspecific piece of machinery and sending it off-site for analysis—oftentaking 3 to 7 days to get results back, which are additionally prone tohuman error.

Embodiments of the present disclosure include collecting opticalspectroscopy data from fluid samples such as oil and sending that datato an analytic system that then determines fluid/oil characteristicsand/or identifies potential issues with a particular piece of machinery.Monitored conditions may include determining a presence of a wear metalin the oil, the presence of an amount of an additive in the oil, thepresence of water in the oil, the total acid number (TAN) of the oil,the total base number (TBN) of the oil, the presence of coolant in theoil, the presence of fuel in the oil, and/or the particle count ofparticulate matter (e.g., soot and other particles) within the oil. Forexample, specific engine problems, such as a bearing that is wearing ora gasket that is leaking, may be identified based on specific materials(e.g., particular wear metals) identified in an engine oil. Additionalvariables (e.g., temperature, pressure, and viscosity of the fluid/oil)may be monitored and data associated with these variables may beanalyzed in conjunction with spectroscopic information to furthercharacterize conditions of the fluid/oil.

Embodiments of the present disclosure include hardware that directlycouples to a piece of machinery (e.g., an engine), and collects spectraldata, and other data characterizing a fluid, in-situ, while themachinery is in operation. The collected data is then analyzed usingmachine learning computational techniques and compared with an evolvingcollection of reference data stored in one or more databases. Forexample, machine learning models that characterize various knownmaterials in a fluid may be built and stored in a database. Such modelsmay be constructed by using machine learning techniques to identifycomposition dependencies of spectral features for well-characterizedtraining data.

Training data may include spectroscopic data for a plurality of samplesof a fluid/oil having known concentrations of an impurity of contaminantof interest as characterized by an analytical laboratory usingconventional analytical techniques. Spectral training data may beobtained for contamination targets, such as fuel or coolantcontamination, by producing physical samples having known concentrations(e.g., serial dilution) of fuel or coolant. Degradation samples, whichare positive for a specific degradation target (e.g., soot, wear metal,etc.) may be obtained from an analytical laboratory that evaluates usedoil samples though conventional means. Samples obtained from ananalytical laboratory may be completely characterized using a battery ofconventional analytical techniques. Resulting machine learning modelsmay include classifier models, decision tree models, regression models,etc.

Then, spectroscopic data that is gathered, in-situ, in real-time (i.e.,while equipment is operating) may be analyzed using similar machinelearning techniques to determine correlations with the stored models todetermine a presence of one or more known components within theotherwise unknown mixture of materials found in the fluid or lubricatingoil of the operating machine. For example, a classifier model may beused to predict whether data from newly analyzed sample has aconcentration above or below a predetermined threshold for one or morecontaminants of interest (e.g., soot, coolant, fuel, etc., in the oil).

Such analytical methods may allow preventive measures to be taken (e.g.,by an operator or automatically by a control system) to avoid criticalfailures and to promote proper functioning, performance, and longevityof operating machinery through the use of informed proactive operationand maintenance practices based on the analysis of the fluid condition.

As described in greater detail below, a fluid analysis system may beprovided that performs Raman spectroscopic measurements to detectmolecular vibrational characteristics of opaque fluids such as motoroil. The system may use a Raman probe and a Raman sub-sampling system.The system may also include multiple excitation sources, a detectionsystem, and an optical switch, as well as power, and control circuitryhoused in a single enclosure that is provided with active coolingsystems. The system may collect, process, and analyze data from multiplefluid sources. One or more analytical systems may be provided thatanalyze such data using machine learning computational techniques todetermine fluid conditions, in-situ, in real-time (i.e., while a pieceof machinery is in operation).

Embodiments of the present disclosure, which are discussed in detailherein, include a Raman spectral excitation and detection system that isdirectly coupled to operating machinery that gathers Raman spectral datafrom working fluids, in-situ, while the machine is operating (i.e., inreal time). Disclosed systems further include an analytical system thatperforms fluid analysis using machine learning techniques to determinethe composition of the working fluids.

Raman spectroscopy allows determination of spectral characteristics inthe ultraviolet, near-infrared, and infrared spectrum. Accordingly, abroad array of target materials may be optically identified using asingle technique. In this regard, Raman spectroscopy provides advantagesover other spectroscopic techniques, including techniques that are basedon the use of infrared and near infrared radiation. Traditionally,application of Raman spectroscopy has not been used to analyze complexfluids such as opaque fluids (e.g., motor oil) because Ramanspectroscopy can produce auto-fluorescence signals that often dominateand essentially mask the Raman signal, particularly in opaque fluidsamples.

Disclosed embodiments of the present disclosure, including systems,methods, and computer program products, provide improved fluid analysiscapabilities that include Raman spectroscopy techniques that arereliably and efficiently used for analysis of opaque fluids such asmotor oil. For example, disclosed methods provide a power calibrationtechnique that overcomes conventions problems associated with usingRaman spectroscopy techniques to investigate chemical compositions(i.e., specific targets including wear metals, soot, etc.) in motor oil.A disclosed power calibration technique determines an optimal intensitylevel of incident radiation to generate a suitable Raman signal whileavoiding auto-fluorescence effects. Analytic models disclosed herein maythen be used to analyze resulting Raman spectral data, as well as otherfluid data (e.g., temperature, viscosity, etc.) and other optical sensorinformation to identify a variety of contaminants, wear metals, oildilution fluids, etc., to allow prediction and diagnosis of fluidconditions. Analytical models may also take into account fluorescenceand absorbance spectral data along with Raman spectral data to provide acomplete characterization of fluids of interest.

FIG. 1 is a schematic of a fluid analysis and monitoring system 10,according to an example embodiment of the present disclosure. System 10includes one or more fluid sources 200 and a spectroscopy system 16 thatare operationally coupled (e.g., optically coupled, mechanicallycoupled, electrically coupled, electromechanically coupled, and/orelectro-optically coupled). In this regard, a coupling assembly 14 mayprovide a mechanical and fluidic coupling between fluid source 200 andspectroscopy system 16. Coupling system 14 may additionally provideelectrical and optical coupling between fluid source 200 andspectroscopy system 16. As such, coupling system 14 may include variouscoupling mechanisms and/or coupling devices, including tubing, fittings,optical fiber cables, etc.

As described in greater detail below, spectroscopy system 16 may performspectroscopy measurements on fluids provided by fluid source 200.Spectroscopic data determined by spectroscopy system 16 may thentransferred to other devices via a wired or wireless network 20 throughwired or wireless links 22 a and 22 b. Various user devices 26 a, 26 b,26 c, etc., may communicate with spectroscopy system 16 via network 20to perform data analysis operations and to provide command and controlinstructions to spectroscopy system 16. Spectroscopy system 16 mayfurther communicate with one or more analytic systems 24 via network 20through wired or wireless links 22 a and 22 b. Spectroscopy system 16may further communicate directly with analytic system 24 through one ormore direct wired or wireless links 22 c.

Analytic system 24 may perform a statistical analysis on data receivedfrom spectroscopy system 16 to determine conditions of the fluid/oil.For example, analytic system 24 may determine a chemical composition ofthe fluid. Analytic system 24 may further determine a concentration ofvarious contaminants in the fluid. Analytic system 24 may be implementedin a variety of ways. In a non-limiting example, analytic system 24 maybe implemented as a circuit element in hardware, or may be implementedin firmware or software of a computing system. Analytic system 24 may beimplemented on a local computing device or may be implemented in a cloudbased computing platform using cloud based tools. In a furtherembodiment, analytic system 24 may be implemented in a data center orother server based environment using a service provider's tools or usingcustom designed tools.

According to an embodiment, fluid source 200 may be a mechanical devicesuch as an engine, generator, turbine, transformer, etc., that employs afluid (e.g., an oil) as a lubricant, as a hydraulic working fluid, etc.An example of an engine may be an internal combustion engine. Fluidsource 200 may be a single engine or may include groups of differenttypes of engines. Example engines may include one or more of: atwo-stroke engine, a four-stroke engine, a reciprocating engine, arotary engine, a compression ignition engine, a spark ignition engine, asingle-cylinder engine, an in-line engine, a V-type engine, anopposed-cylinder engine, a W-type engine, an opposite-piston engine, aradial engine, a naturally aspirated engine, a supercharged engine, aturbocharged engine, a multi-cylinder engine, a diesel engine, a gasengine, or an electric engine. In other embodiments, system 10 for afluid analysis and monitoring system may include various other fluidsources 200. In other embodiments, fluid source 200 may be associatedwith an oil drilling operation, an oil refinery operation, a chemicalprocessing plant, or other industrial application for which fluidmonitoring may be desired.

FIG. 2 is a schematic of a spectroscopy system 5000 a connected to afluid source 200 a, according to an example embodiment of the presentdisclosure. System 5000 a may include a sample chamber 5330 that isfluidly connected to fluid source 200 a. A valve 5020 may be provided tocontrol flow of fluid into sample chamber 5330 from fluid source 200 a.Fluid source 200 a may provide fluid to sample chamber 5330 throughactuation of valve 5020.

System 5000 a may further include a spectroscopy system 16 a that mayinclude an excitation source 5344 that generates electromagneticradiation and a detection system 5346 that detects electromagneticradiation. Excitation source 5344 and detection system 5346 may behoused in an enclosure 5002 a. Excitation source 5344 may be opticallycoupled to an optical probe 5342 via fiber optic cables 5348 a.Similarly, detection system 5346 may be optically coupled to opticalprobe 5342 via fiber optic cables 5348 b. Optical probe 5342 may beoptically coupled to sample chamber 5330. Electromagnetic radiationgenerated by excitation source 5344 may be provided to optical probe5342 which may couple the electromagnetic radiation into sample chamber5330.

Electromagnetic radiation, provided to the fluid in sample chamber 5330by optical probe 5342, may interact with fluid in sample chamber 5330.Upon interaction with the fluid sample, electromagnetic radiation may bereflected, absorbed, scattered, and emitted from the fluid. Thescattered and emitted radiation may then be received by optical probe5342 and provided to detection system 5346 via fiber optic cables 5348b. As described in greater detail below, the reflected, absorbed, andemitted radiation depends on the composition of the fluid in fluidchamber 5330. As such, properties of the fluid may be determined byanalyzing intensities of reflected, absorbed, scattered, and emittedradiation at various frequencies relative to a frequency spectrum ofincident radiation generated by the excitation source 5344.

As described in greater detail below, optical probe 5342 may be Ramanprobe (e.g., see FIG. 25B) that may be used in a Raman sub-samplingsystem (e.g., see FIG. 25A), a reflection probe (e.g., see FIG. 26B)that may be used in a fluorescence sub-sampling system (e.g., see FIG.26A) or in an absorbance/fluorescence/scatter sub-sampling system (e.g.,see FIG. 29), or a transmission dip probe (e.g., see FIG. 27B), that maybe used in an absorbance sub-sampling system (e.g., see FIG. 27A), in anabsorbance/fluorescence/scatter sub-sampling system (e.g., see FIG. 19)or used in a Fourier Transform Infra-Red (FTIR) absorbance sub-samplingsystem (e.g. see FIG. 28A).

FIG. 3 is a schematic of a spectroscopy system 5000 b connected to afluid source 200, according to an example embodiment of the presentdisclosure. System 5000 b of FIG. 3 and system 5000 a of FIG. 2 have anumber of elements in common, while system 5000 b of FIG. 3 includesadditional sensors.

Like the fluid analysis system 5000 a of FIG. 2, system 5000 b of FIG. 3includes sample chamber 5330 that is fluidly connected to fluid source200. Sample chamber 5330 may include one or more valves, 5020 a and 5020b, that control flow of fluid into sample chamber 5330. System 5000 bsimilarly includes excitation source 5344 optically coupled to anoptical probe 5342 via fiber optic cables 5348 a, and detection system5346 optically coupled to optical probe 5342 via fiber optic cables 5348b. According to an embodiment, excitation source 5344, detection system5346, and control system 5380 (described below) may be housed in anenclosure 5002 b. Optical probe 5342 couples electromagnetic radiationgenerated by excitation system 5344 into a fluid within sample chamber5330. Optical probe 5342 similarly receives scattered and emittedradiation from sample chamber and provides it to detection system 5346.

In various embodiments, shut-off values, 5320 a and 5320 b, may beincluded on either side of optical probe 5342. Shut-off valves 5320 aand 5320 b may be manually or electronically controlled. In anembodiment in which the shut-off valves 5320 a and 5320 b areelectronically controlled, a voltage may be supplied to valves 5320 aand 5320 b via an electrical connector (not shown). Shut-off valves 5320a and 5320 b may be configured to open in response to the appliedvoltage. Shut-off valves 5320 a and 5320 b may be further configured toautomatically close in response to removal of the applied voltage. Assuch, shut-off valves 5320 a and 5320 b remain closed unless the fluidanalysis system is engaged.

In contrast to system 5000 a of FIG. 2, system 5000 b of FIG. 3 mayfurther include additional sensors. For example, system 5000 b mayinclude a temperature sensor 5310 configured to measure a temperature ofthe fluid. System 5000 b may further include a viscometer 5328configured to measure a viscosity of the fluid. Temperature sensor 5310and viscometer 5328 may be electrically connected to controller 5380 viaand electrical connector 5329. Controller 5380 may be configured toprocess signals received from temperature sensor 5310 and viscometer5328 to generate temperature and viscosity data. Such temperature andviscosity data may be used in further embodiments in comparison withinput parameters to controller 5380 that may be configured to performother control operations based on the input temperature and viscositydata. For example, in other embodiments described below, heating andcooling elements (not shown) may be provided that may add or subtractheat from the system to control a temperature, etc., based ontemperature measurements determined by temperature sensor 5310.

According to an embodiment, controller 5380 may be a Controller AreaNetwork (CAN) system that may be configured to communicate withtemperature sensor 5310 and viscometer 5328 using digital signals. Forexample, temperature sensor 5310 and viscometer 5328 may communicate towith a microprocessor (not shown) via a CAN communication system.Messages associated with communications between sensors (e.g.,temperature sensor 5310 and viscometer 5328) may include a CAN ID. TheCAN ID may be used in determining what actions may be taken regardingspecific communications. In other embodiments, temperature sensor 5310and viscometer 5328 may communicate with a microprocessor by supplying acommunication address (e.g., a MAC address, and IP address, or anothertype of physical address).

In further embodiments, system 5000 b of FIG. 3 may contain additionalsensors (not shown). For example, system 5000 b may further includepressure sensors, fluid flow meters, moisture/humidity sensors, and pHsensors. System 5000 b of FIG. 3 may further include sensors configuredto detect particulates in the fluid. Such particulate sensors may beconfigured to detect large or small particles, where particles areconsidered to be large or small when they are larger or smaller than apredetermined size threshold.

FIG. 4 is a schematic of a spectroscopy system 5000 c connected to afluid source 200, according to an example embodiment of the presentdisclosure. Unlike system 5000 a of FIG. 2 and system 5000 b of FIG. 3,system 5000 c of FIG. 4 is configured to measure absorption ofelectromagnetic radiation. In this regard, systems 5000 a and 5000 b,shown in FIGS. 2 and 3, respectively, used a single optical probe 5342to couple electromagnetic radiation into and out of sample chamber 5330.As such, electromagnetic radiation received by optical probe 5342, viafiber optic cables 5348 b, from sample chamber 5330 in systems 5000 aand 5000 b (e.g., see FIGS. 2 and 3, respectively) is of the form ofreflected (i.e., scattered and/or emitted radiation) radiation.

In contrast, system 5000 c shown in FIG. 4 includes two probes 5342 aand 5342 b. Optical probe 5342 a is configured to receiveelectromagnetic radiation generated by excitation source 5344 via fiberoptic cables 5348 a and to couple the received radiation into the fluidin sample chamber 5330. Optical probe 5342 b is configured to receiveelectromagnetic radiation that has been transmitted through samplechamber after such radiation has interacted with the fluid in samplechamber 5330. Electromagnetic radiation received by optical probe 5342 bis transmitted to detection system 5346 via fiber optic cable 5348 b. Aspectrum of radiation transmitted through the fluid in sample chamber5330, as measured by system 5000 c, may have different spectral featuresfrom radiation reflected from the fluid as measured by systems 5000 aand 5000 b, of FIGS. 2 and 3, respectively. As such, spectroscopic datameasured by system 5000 c of FIG. 4 provides complementary informationto that provided by systems 5000 a and 5000 b shown in FIGS. 2 and 3.

Other components of system 5000 c not specifically described withreference to FIG. 4, (e.g., control system 5380, source 200, valves 5020a and 5020 b, temperature sensor 5310, and viscometer 5328) are similarto corresponding components of system 5000 b of FIG. 3. As with system5000 c of FIG. 3, excitation source 5344, detection system 5346, andcontrol system 5380, of system 5000 c (of FIG. 4) may be housed in anenclosure 5002 c.

FIG. 5 is a schematic of a spectroscopy system 5000 d connected to fluidsources 200 a and 200 b, according to an example embodiment of thepresent disclosure. In this regard, the single engine fluid monitoringsystem (e.g., systems 5000 a, 5000 b, and 5000 c, of FIGS. 2, 3, and 4,respectively) may be expanded to include a multi-engine monitoringsystem (e.g., system 5000 d of FIG. 5) capable of monitoring a pluralityof engine arranged in an array in various locations. Multi-enginemonitoring systems may allow fluid monitoring on any multi-engineequipment. Specific examples of multi-engine systems that may benefitfrom the disclosed systems, apparatus and methods may includemulti-engine ships, vessels, barges, tankers, airplanes, industrialequipment, wind farms, solar arrays, and the like.

A multi-engine configuration may require additional features orcomponents. For example, a multi-engine configuration may include anoptical switch 5390 (as described in greater detail below) to routeelectromagnetic radiation from a single excitation source 5344 to one ofN number of outputs (i.e., multiple engines), as described in greaterdetail below. In certain embodiments, a degradation or reduction ofsignal may be associated with an optical switch. Despite the degradationor reduction of excitation signal that may occur when an optical switchis employed, use of an optical switch provides greater system control.

Fluid source 200 a may be fluidly coupled to sample chamber 5330 a andfluid source 200 b may be fluidly coupled to sample chamber 5330 b.Sample chamber 5330 a may include a valve 5020 a. Similarly, samplechamber 5330 b may include a valve 5020 b. System 5000 d may include anexcitation source 5344 and a detection system 5346 configured togenerate and detect electromagnetic energy, respectively, as describedabove. According to an embodiment, excitation source 5344, detectionsystem 5346, and optical switch 5390 may be housed in an enclosure 5002d.

As mentioned above, system 5000 d further includes an optical switch5390. Optical switch 5390 is optically connected to excitation source5344 via fiber optic cable 5348 e. Optical switch 5390 receiveselectromagnetic radiation from excitation source 5344 via fiber opticcable 5348 e and may provide such radiation to optical probe 5342 a viafiber optic cable 5348 a. Similarly, optical switch 5390 may provideelectromagnetic radiation to optical probe 5342 b via fiber optic cable5348 b. Optical switch 5390 may be configured to selectively provideradiation to optical probe 5342 a only, to optical probe 5342 b only, orto both probes 5342 a and 5342 b.

Optical components may be connected to one another via optical cableshaving an appropriate diameter. In one embodiment an optical fiberconnection may connect an electromagnetic radiation source (e.g., alaser) and an optical switch to an optical excitation fiber having adiameter of about 100 μm. In one embodiment an optical fiber connectionmay connect an optical switch to an optical emission fiber having adiameter of about 200 μm. In one embodiment an optical switch may beconfigured with one or more optical fibers having diameters of about 50μm. In one embodiment an optical combiner may be configured with one ormore optical fibers having diameters of about 200 μm. In furtherembodiments, various other diameter fibers may be used. For example,similar data throughput may be obtained with larger diameter fibers anddecreased acquisition time. Similarly, smaller diameter fibers may beused with increased acquisition time to achieve a comparable datathroughput.

Optical switch 5390 may further be configured to receive reflected,scattered, and emitted radiation from optical probe 5342 a via fiberoptic cable 5348 c and to receive reflected, scattered, and emittedradiation from optical probe 5342 b via fiber optic cable 5348 d.Optical switch may then provide the received electromagnetic radiationto detection system 5346 via fiber optic cable 5348 f.

Optical switch 5390 may be configured to selectively receive radiationfrom optical probe 5342 a only, from optical probe 5342 b only, or fromboth probes 5342 a and 5342 b. In further embodiments, fluid analysisand monitoring systems, similar to system 5000 d of FIG. 5 may beconfigured to receive fluid from three fluid sources, from four fluidsources, etc. In other embodiments additional fluid sources may bemonitored. While there is no specific limit to the number of fluidsources that may be monitored, in one specific embodiment, a rotaryoptical switch may be employed to detect up to thirty-two (32) separatefluid sources. According to an embodiment, optical switch may beconfigured to make electrical as well as optical communication withexcitation source 4344 and a detection system 5346 and with a controlsystem (e.g., see 5380 of FIG. 4).

FIG. 6 shows a sample chamber 5330 with sensors, according to an exampleembodiment of the present disclosure. FIG. 6 provides more realisticdetail to embodiments schematically illustrated, for example, in FIG. 3.In this regard, FIG. 6 illustrates sample chamber 5330 that may beconnected to a fluid source (e.g., fluid source 200 in FIG. 3) via TAPconnector 5010. According to an embodiment, TAP connector 5010 may be a¼ inch NPT, while in other embodiments, TAP connector 5010 may be anyother diameter sufficient to connect to a fluid source.

FIG. 6 further illustrates a T-shaped optical sampling chamber 5335,which is described in greater detail below with reference to FIG. 7.T-shaped optical sampling chamber 5335 has a configuration in whichfluid may flow thorough the sampling chamber 5335. In anotherembodiment, a sampling chamber may be provided that has a shape otherthan a T-shape but nonetheless allows fluid to flow through the chamber.In further embodiments, a sampling chamber may be provided that has a“deadhead” configuration, that is, having only a single inlet and nooutlet (e.g., as shown schematically in FIGS. 2 to 5). Such a deadheadconfiguration may act as an optical port (that may include an opticalprobe) that may be connected directly into an engine galley.

T-shaped optical sampling chamber 5335 may further include a metal probesleeve 5610 used to make a mechanical and optical connection to opticalprobe 5342. In this regard, optical probe 5342 may be inserted intoprobe sleeve and sealed into place in order to provide a closed opticalsample system that is optically accessible to various sources ofelectromagnetic radiation (e.g., excitation source 5344 of FIG. 3).Optical probe 5342 is also shown as optically connected with a fiberoptic cable 5348, which may represent one or more of fiber optic cables5348 a and/or 5348 b described above with reference to FIG. 3, forexample.

Optical probe 5342 is described in greater detail below with referenceto FIGS. 9A to 10B. For example, as described below, optical probe 5342may have a shutter 5350 and probe window 5352 that may be eliminated inother embodiments. FIG. 6 further illustrates details of temperaturesensor 5310, viscometer 5328, and electrical connector 5329. In furtherembodiments, sample chamber 5330 may include valves and various othersensors (not shown), as described below with reference to FIG. 12.

FIG. 7 shows an enlarged cross-sectional view of the T-shaped opticalsampling chamber 5335 shown in FIG. 6, according to an exampleembodiment of the present disclosure. T-shaped optical sampling chamber5335 may include an optical window 5600 to facilitate opticalmeasurements of fluid samples. Optical window 5600 may be translucent ortransparent and may be formed using any transparent material. In aspecific embodiment, the optical window may be formed using sapphireglass, for example.

Optical window 5600 may be inserted into a wall of sample chamber 5335.Further, optical window 5600 may be sealed into the wall of samplechamber 5335 using one or more gaskets and or sealing materials (e.g.,epoxy, o-rings, etc.). T-shaped optical sampling chamber 5335 mayinclude metal probe sleeve 5610 where optical probe 5342 may be insertedand sealed into place to provide a closed optical sample system. In thisregard the closed optical sample system may be optically accessible tovarious EM excitation sources. In other embodiments, sample chamber 5335may be configured to include a plurality of optical sample chamberwindows 5600 (not shown) configured to accommodate operationalattachment of a plurality of probes 5342.

FIG. 8A is an isometric view of a sample chamber 5336, according to anexample embodiment of the present disclosure. Sample chamber 5336 mayinclude optical window 5600 a that may be made of a transparent materialsuch as sapphire glass. Optical window 5600 may have a ⅜ inch to 1 inchdiameter. Other embodiments may include optical windows having otherdiameters and made of other materials. Chamber 5336 may further includea fluid connector 5605 that may be ½ inch NPT connector. Fluid connector5605 may be used, for example, to remove air bubbles from the system.Chamber 5336 may further include an end cap 5607 that may be used toseal an end of chamber 5336.

FIG. 8B is a top view of the sample chamber 5336 of FIG. 8A, accordingto an example embodiment of the present disclosure. FIG. 8B shows theabove-described optical window 5600 a, fluid connector 5605, and end cap5607.

FIG. 8C is a partially exploded side view of the sample chamber 5336 ofFIG. 8A, according to an example embodiment of the present disclosure.In this view, optical window 5600 a and fluid connector 5605 (e.g.,shown in FIGS. 8A and 8B above) have been removed. Further, end cap 5607is shown in a disassembled state. In this regard, end cap 5607 includesa threaded sealing cap 5607 a and an interlock device 5607 b.

FIG. 8D shows a portion of a sampling chamber 5336 a with ports for anoptical probe and a viscometer, according to an example embodiment ofthe present disclosure. FIG. 8D illustrates a side wall section 5336 aof sample chamber 5336 (e.g., see FIG. 8A). Side wall section 5336 aincludes a first port 5336 b and a second port 5336 c. One or moresensor probes (e.g., temperature sensor 5310, viscometer 5328, shown inFIGS. 3, 4, and 6) may be configured as a threaded plug 5336 d. Threadedplug 5336 d may be configured to be installed into port 5336 b or 5336 cby screwing threaded plug 5336 d into threaded portions (not shown) ofport 5336 b or 5336 c.

FIG. 9A shows an optical probe 5342 connected to a portion of a T-shapedoptical sampling chamber 5335 a, according to an example embodiment ofthe present disclosure.

FIG. 9B shows a partial cross-sectional view of a straight-line samplingchamber 5335 b connected to an optical probe 5342, according to anexample embodiment of the present disclosure.

In FIGS. 9A and 9B optical probe 5342 is shown having a shutter 5350.The shutter allows for observation and/or inspection of the internalarea of the optical probe through an optical probe window 5352. Tominimize the potential for exposure of humans to high energy laserlight, the depicted shutter 5340 and probe window 5352 may be eliminatedin other embodiments.

In an example embodiment, optical probe 5342 may be a Raman probe. Ramanspectroscopy is a spectroscopic technique that determines informationabout molecular vibrations of a sample. Determined information regardingmolecular vibrations may then be used for sample identification andquantitation. The technique involves providing incident electromagneticradiation (e.g., using a laser) to a sample and detecting scatteredradiation from the sample. The majority of the scattered radiation mayhave a frequency equal to that of the excitation source (e.g.,excitation source 5344 of FIG. 3). Such scattered radiation is known asRayleigh or elastic scattering.

A small amount of the scattered light may be shifted in frequency fromthe incident laser frequency due to interactions between the incidentelectro-magnetic waves (i.e., photons) and vibrational excitations(i.e., induced transitions between vibrational energy levels) ofmolecules in the sample. Plotting intensity of this frequency-shiftedradiation vs. frequency, or equivalently vs. wavelength, results in aRaman spectrum of the sample containing Raman shifted peaks. Generally,Raman spectra are plotted with respect to the laser frequency so thatthe Rayleigh band lies at 0 cm⁻¹. On this scale, band positions (i.e.,peaks of the spectrum) lie at frequencies that correspond to differencesin vibrational energy levels of various functional groups. Typicallyfrequencies are expressed in wavenumber units of inverse centimeters(cm⁻¹), as defined below.

In FIG. 9A, optical probe 5342 interfaces with optical window 5600 of aT-shaped sampling chamber 5335 a to provide electromagnetic radiation tosampling chamber 5335 a. Optical probe 5342 may be coupled to T-shapedsampling chamber 5335 a via probe sleeve 5610. Electromagnetic radiationentering T-shaped sampling chamber 5335 a through optical window 5600forms a radiation pattern having a focal point 5602 a within the liquid,as indicated in the cross-sectional view of T-shaped sampling chamber5335 a of FIG. 9A.

FIG. 9B shows optical probe 5342 interfacing with an optical chamber5335 b having a straight-line configuration. Optical probe 5342 may becoupled to straight-line optical chamber 5335 b via probe sleeve 5610.Optical probe 5342 interfaces with optical window 5600 to provideelectromagnetic radiation to straight-line sample chamber 5335 b.Electromagnetic radiation entering straight-line sampling chamber 5335 bthrough optical window 5600 forms a radiation pattern having a focalpoint 5602 b within the liquid, as indicated in the cross-sectional viewof straight-line sampling chamber 5335 b of FIG. 9B. As described above,depicted shutter 5350 and probe window 5352 may be eliminated in otherembodiments.

FIG. 10 shows optical probe 5342 and a cover 5343, according to anexample embodiment of the present disclosure. Optical probe 5342 isconfigured to interface with a sampling chamber, as described above.Optical probe 5342 may have an optional shutter 5350 (e.g., see FIGS. 6,9A, 9B, and FIG. 10). As described above, optical probe 5342 mayinterface with T-shaped sample chamber 5335 a, shown in FIG. 9A, or withstraight-line sample chamber 5335 b, shown in FIG. 9B. In an exampleembodiment, optical probe 5342 may be a Raman probe. In otherembodiments, optical probe 5342 may be various other probes, asdescribed in greater detail below. As shown in FIG. 10, optical probe5342 may further include an optional probe cover 5343 that is configuredto protect optical probe 5342 when optical probe 5342 is not installedwith T-shaped sample chamber 5335 a or with straight-line sample chamber5335 b.

FIG. 11A is a partial cross-sectional view of a fluid source with anoptical immersion probe 403 and a viscometer 600 coupled to the fluidsource, according to an example embodiment of the present disclosure. Inthis example, immersion probes are configured to measure optical andother properties in a single fluid source. In contrast to otherembodiments described above, the embodiment of FIG. 11A does not requirea sample chamber. In this embodiment, optical probe 403 may be animmersion probe (e.g. ball probe) that is configured to be inserteddirectly into a fluid source apparatus 500. In this regard, opticalprobe 403 may make direct operational contact with a fluid (e.g., anoil) sample 550 within source apparatus 500. Optical probe 403 may besecurely attached to source apparatus 500 via a threaded connection 410a. Optical probe hardening may preclude undesirable effects from variousfactors including dust, high temperature, low temperature, water,humidity, and/or vibrational damage. In some embodiments, an opticalprobe 403 may be configured to withstand temperatures of up to 250° C.As described above, optical probe 403 may receive electromagneticradiation via fiber optic cable 5348 a and may transmit (i.e., return)electromagnetic radiation via fiber optic cable 5348 b.

Optical (immersion) probe 403 may also be configured to include achassis 420. Optical probe chassis 420 may be configured to furthersecure optical probe 400 to source apparatus 500. Optical probe chassis420 may be further “hardened” to withstand the stress of extremeenvironmental conditions. Optical probe chassis 420 may be configured toinclude hardening features to provide protection against vibrationalstress, dust, and extreme heat and/or cold when optical probe 400 isconnected to source apparatus 500.

An optical probe chassis 420 may be formed from any suitable material.Examples of suitable materials to form the chassis 420 of an immersionprobe for direct immersion within the flow of an oil within an sourceapparatus include carbon steel, alloy-20, stainless steel, marine-grade316 stainless steel, Hastelloy C276™ alloy, which provides corrosionresistance, etc. In one embodiment, a chassis 420 of optical probeincluding an immersion probe as described herein may be formed fromstainless steel and may include compression fittings, couplings, and/ormanifolds that permit or otherwise facilitate quick connection of thechassis 420 to conventional ports present on a source apparatus, such asan engine.

For instance, optical probe chassis 420 may include a fitting, acoupling, and/or a manifold to permit or otherwise facilitate connectionto a source apparatus having port diameters ranging from about 1/16thinch to about 2 inches. In certain embodiments, optical probe chassis420 may include a tubular member having a uniform diameter having amagnitude in a range from about ¼ inch to about ½ inch. In addition orin some embodiments, the optical probe chassis 420 may include adaptorsthat permit or otherwise facilitate insertion of optical (immersion)probe 403 into multiple source apparatus ports with differing diameteropenings. Further or in yet other embodiments, optical probe chassis 420may include quick-connection fittings, couplings, and/or manifolds thatpermit or otherwise facilitate simple and rapid removal of optical(immersion) probe 403 from a source apparatus (e.g., an engine). Removalof optical probe 403 from the source apparatus allows easy access forinspection and cleaning as needed.

In certain embodiments, an optical probe 403 that includes a chassis 420may be configured for low pressure applications (e.g., pressure lessthan about 200 psi). In other embodiments, an optical probe 403 thatincludes the chassis 420 may be configured to withstand up to about3,000 psi. Disclosed embodiments employing optical probe 403 configuredfor use under high pressure may require additional modifications tosecure the chassis 420. For high-pressure applications, optical(immersion) probe 403 may be secured or otherwise affixed to chassis 420via a weld. Specifically, as an illustration, a welded ANSI flange sealmay be used to secure optical (immersion) probe 403 to chassis 420.Other probes, such as temperature sensor 5310 and viscometer 5328 (e.g.,see FIG. 3) may similarly be hardened.

In other embodiments, optical probe 400 may be further configured toinclude a spherical lens 430. Spherical lens 430 may be configured tofocus first electromagnetic radiation, transmitted into the fluid/oilsample, to a single focal point 440. Similarly special lens 430 may beconfigured to receive second electromagnetic radiation from thefluid/oil sample at the focal point 440. In this embodiment, there is norequirement to optimize or calibrate a focal path. The use of optical(immersion) probe 403, configured with a spherical lens 430, may allowfaster, simpler installation. Removal of probe 403 for cleaning, orreplacement of one or more parts, may also be simplified at leastbecause there is no focal path calibration required.

Additional forms of data or other types of information may be obtainedfrom the system of FIG. 11A, through use of one or more additionalsensors, according to an embodiment. Data or information that may becollected includes temperature data and/or viscosity data. In thisregard, sensor 600 may allow collection of viscosity data and/ortemperature data. Sensor 600 may be operationally connected to sourceapparatus 500, which may include an engine source, through a port.Sensor may communicate electrically or optically with a controller (notshown) via connection 5329 (e.g., see FIG. 3). As illustrated in FIG.11A, sensor 600 may be securely attached to source apparatus 500 via athreaded connection 410 b. A variety of fasteners may be coupled tothreaded connection 410 b to secure sensor 600 to source apparatus 500.

FIG. 11B shows partial cross-sectional views of two fluid sources 500and 500′ with immersion probes 403 a and 403 b connected to each fluidsource, according to an example embodiment of the present disclosure. Asillustrated in FIG. 11B, immersion probes 403 a and 403 b may beprovided to allow testing and monitoring of fluid samples from aplurality of sources, including source apparatus 500 and sourceapparatus 500′. Such probes, 403 a and 403 b, may be suitable for anembodiment such as system 5000 d, described above with reference to FIG.5. As described above with reference to FIG. 5, first electromagneticradiation from an excitation source may directed to probes 403 a and 403b via fiber optic cables 5348 a and 5348 b, respectively. Similarly,second electromagnetic radiation emitted from the fluid/oil may bereturned to a detection system from immersion probes 403 a and 403 b viafiber optic cables 5348 c and 5348 d, respectively. Similar systems aredescribed in U.S. patent application Ser. No. 15/139,771, the disclosureof which is hereby incorporated by reference in its entirety.

For example, U.S. patent application Ser. No. 15/139,771 discloses amulti-channel fluid monitoring system including an optical switch system(similar to switch 5390 described above with reference to FIG. 5) whichgates or directs first electromagnetic radiation from an excitationsource to a fluid/oil in a source apparatus, and transmits secondelectromagnetic radiation emitted from the oil in the source apparatusto a detection system.

As described above immersion probes 403 a and 403 b may be configured tobe inserted into source apparatus 500 and source apparatus 500′,respectively. In this way, probes 403 a and 403 b may be in direct,operational contact with a fluid/oil sample 550 and fluid/oil sample550′ within source apparatus 550 and 550′, respectively. Optical probe403 a and optical probe 403 b may be securely attached to sourceapparatus 550 and source apparatus 550′ via threaded connections 410 and410′, respectively. Optical probe chassis 420 and optical probe chassis420′ may be configured to further secure optical probe 403 a and opticalprobe 403 b to source apparatus 500 and source apparatus 500′,respectively. Optical probe chassis 420 and optical probe chassis 420′may be configured to include vibrational, dust, and heat protection whenoptical probe 403 a and optical probe 403 b are connected to sourceapparatus 500 and source apparatus 500′, respectively.

Optical probe 403 a and 403 b may be further configured to includerespective spherical lenses 430 and 430′. Spherical lens 430 andspherical lens 430′ (e.g., lenses associated with a ball probe) focusthe first electromagnetic radiation transmitted into the fluid/oilsample to respective single focal points 440 and 440′. Similarly, secondelectromagnetic radiation may be received from the oil sample atrespective single focal points 440 and 440′. Disclosed embodiments,therefore, include a focal path that does not require optimization orcalibration. The use of an optical probe 403 a or 403 b, configured withspherical lenses 430 and 430′, respectively, may allow faster, simplerinstallation. Removal of probes 403 a and 403 b for cleaning, orreplacement of one or more parts is also simplified since there is nofocal path calibration required.

FIG. 12 is a schematic of a fluid analysis system, according to anexample embodiment of the present disclosure. In contrast to systemsdescribed above having only a fluid inlet (e.g., systems 5000 a, 5000 b,5000 c, 5000 d, illustrate in FIGS. 2 to 5), system 1200 includes afluid inlet 1202 and a fluid outlet 1204. Fluid may enter system 1200through fluid inlet 1202, may flow through a fluid passage 1206, and mayexit through fluid outlet 1204.

System 1200 may include one or more optical probes 1208 connected to anexternal excitation source via fiber optic cable 5348 a and to anexternal detection system via fiber optic cable 5348 b. System 1200 mayinclude additional sensors 1210 such as a temperature sensor orviscometer. As described above, optical probe 1208 may be connected toan external excitation/detection system 1212. Additional sensors 1210may further be connected to a control system 1214. Control system 1214may include a CAN bus. CAN bus may be connected to various externaldevices via CAN connectors 1215 Further sensors may include one or morepressure sensors 1216 a, 1216 b, and 1216 c, as well as one or more flowmeters 1218 a, 1218 b, and 1218 c. System 1200 may further includeadditional valves 1220 a, 1220 b, 1220 c, and 1220 d. One of valves 1220d may further include a port to allow a sample of fluid flowing throughsystem 1200 to be manually drawn.

FIG. 13A shows a fluid analysis system 2000, according to an exampleembodiment of the present disclosure. System 2000 may have a fluidflow-through configuration and may have a plurality of sensors,according to an example embodiment of the present disclosure. In thisexample, system 2000 may have one or more a nano chip plugs 1032 a and1032 b along with other sensors. As shown, fluid analysis system 2000with the nano chip plugs 1032 a and 1032 b may be constructed andconfigured to be installed at an engine oil pressure galley and tothereby bypass the engine back to an oil filler neck. In a bypass loop,thus created, oil may flow (i.e., may be routed) from equipment fluidaccess point, Y, through a programmable flow control valve 2002.

Flow control valve 2002 may be programmed to open and close to allow oilto flow through system 2000. Oil may be stationary in the system 2000once valve 2002 is closed. This option may be added to allow a moredetailed oil sample to be scanned (i.e., spectroscopic data to bemeasured) while the oil is stable and not flowing at pressures of, forexample, 50 psi. Once the scan is complete, valve 2002 may open andallow oil to flow through the system 2000 until valve 2002 is once againclosed for a future sampling time. In exemplary embodiments, this nextsampling time may occur as soon as every 30 seconds. However, thissystem 2000 may be configured to take samples in any other time frame asneeded.

Fluids, such as oil, may be routed through a pump 2004 to providepressure when there is little or no fluid/oil pressure available. Inother embodiments, pump 2004 may not be needed. In various embodiments,oil may then be routed through a pressure reducing valve 2006, oilcooler 2008, and push button oil sample valve 2010 a installed forsampling of the oil before it reaches nano chip plug 1032 a. Otherembodiments (e.g., see FIG. 13B) may include fewer components. Oilcooler 2008 may be used inline if the oil being routed through thesystem 2000 is too hot (i.e., if measured temperature exceeds apredetermined temperature value). From nano chip plug 1032 a, oil may berouted to a 1-μm bypass oil filter 2014 to allow more detailed analysisand to further prolong engine oil life via extra filtration of the oilsample.

In exemplary embodiments, another nano chip plug 1032 b may be addedafter the 1-μm bypass oil filter 2014. The 1-μm oil filter 2014 may beinline of a bypass loop and may take a scan before and after fluid/oilpasses through the filter 2014 in order to compare and determine howwell the filtration is performing and how exactly the filter 2014 isimpacting the fluid/oil sample. This particular configuration is uniquebecause once this additional nano chip plug 1032 b is added, the beforeand after readings (i.e., readings taken before and after the 1-μm oilfilter 2014) may be compared and analyzed. The resulting data may thenbe used to prolong the life of the oil and provide a measurable impactthat the 1-μm oil filter 2014 may be having on the oil. In contrast, itis virtually impossible to show the measurable impact of an oil filter2014 in real-time (i.e., while the engine is running) in existingconventional systems. On the way back to the engine's oil filler neckinto equipment's fluid return point, Z, oil may be passed throughanother push button oil sample valve 2010 b.

Fluid analysis system 2000 may be used to gather samples and/or addrelevant data from the samples to a database. Fluid analysis system 2000may be connected to and to transfer data to a computational node 1004(e.g., see FIG. 15A and related discussion below). Node 1004 may thentransmit the data to a database as described above. The database may belocated on a cloud based computing platform or in any known externaldevice. In some embodiments, the node 1004 itself may house thedatabase.

FIG. 13B shows a fluid analysis system 2000 a, according to an exampleembodiment of the present disclosure. In this example, system 2000 a maybe provided with only a viscometer 5328 and an optical probe 5342.

FIG. 14 shows a nano chip plug of the fluid analysis system of FIG. 13B,according to an example embodiment of the present disclosure. FIG. 14shows interior components of the nano chip plug 1032. Nano chip plug1032 may utilize a spectral scanner/spectrometer 1034 to continuouslyscan and inform a user of the molecular makeup and condition of anyindustrial fluid/oil. Nano chip plug 1032 may further include anexcitation source 1038 and a detection system 1039. Nano chip plug 1032may further include control and communication circuitry 1036.

In exemplary embodiments, nano chip plug 1032 may have a size less thanapproximately 1 inch×1 inch. In other embodiments, the nano chip plug1032 may have other sizes and configurations to perform real-time oilanalysis (i.e., while an engine is operating). In exemplary embodiments,nano chip oil plug 1032 may be used for real-time oil analysis (i.e.,while the engine is running) by integrating a nano chip and spectrometer1034 into an oil plug, as illustrated, for example in FIG. 14. The oilplug may be any plug that may access the fluid being analyzed. In anexemplary embodiment, an existing oil plug in an engine/equipment may beremoved, and a nano chip oil plug 1032 may be installed onto theengine/equipment in place of the existing oil plug.

FIG. 15A shows a fluid analysis system 1000, according to an exampleembodiment of the present disclosure.

FIG. 15B shows a node 1006 that may be used with the fluid analysissystem of FIG. 15A, according to an example embodiment of the presentdisclosure.

Fluid analysis system 1000 may include an enclosure 1002 having a femalepipe thread inlet 1016 and outlet 1018. In an exemplary embodiment,enclosure 1002 may be an 18 inch×18 inch×6 inch metal enclosure, andinlet 1016 and outlet 1018 may be ¼ inch inlets and outlets,respectively. In other embodiments, suitable other dimensions may beemployed. In various embodiments, the inlet of enclosure 1002 mayinclude a shut-off valve 1020 for safety (e.g., allowing fluid to beshut off in case a line is found to be leaking), and/or for maintenancethat may need to be performed on the enclosure 1002 without having toshut the system 1000 down. Additionally, enclosure 1002 may include areset switch 1014 on one side for manual reset of an engine/equipment(to which system 1000 may be coupled) after an oil change has beenperformed to establish a new baseline for oil analysis.

Enclosure 1002 may also include a controller 1012 configured to controla plurality of sensors, as illustrated in FIG. 15A. For example, incertain embodiments, controller 1012 may control up to 36 fluid analysissensors. Fluid analysis sensors may be mounted within enclosure 1002.For example, enclosure 1002 may include multiple types of oil analysissensors, including but not limited to sensors with the followingproperties: oil property monitoring capabilities, and/or identificationof specific wear metals 1022, moisture levels 1024, particulate counts1026, viscosity 1028, TAN, TBN, Nitration, Sulfation, Foreign Oils,Solvents, Glycol, Soot, Dissolved Gases, and/or Oil Additive Depletion(Zn, Mo, Pd, Ca, Mg, Ba, Na).

Sensors may be programmed to periodically communicate data to controller1012. For example, data may be communicated every few milliseconds,every second, every two seconds, every ten seconds, every minute, everyfew minutes, etc., to controller 1012. Further, controller 1012 may beconfigured to store data for a lifespan of five years or longer. In someembodiments, sensors may be provided that may be easily changeable ifreplacement is required. To replace a sensor, shut off valve 1020 may beused to shut off fluid flow. A front cover panel may then be opened anda sensor, needing replacement, may be unscrewed and removed from thefemale pipe thread. A new sensor may then be replaced in a similarmanner by screwing the new sensor into a sensor connector having afemale pipe thread. Controller 1012 may be configured to automaticallyrecognize a new sensor and to begin collecting data using the newlyinstalled sensor.

In some embodiments, enclosure 1002 may include an electric pump (notshown) that may draw oil out of the attached equipment/engine (i.e.,fluid/oil source 200 of FIGS. 1 to 4), and may push the oil through theenclosure 1002 and back to the equipment/engine (i.e., back to thefluid/oil source 200). Pump may be a 120V, 240V, or 480V electricalpump, for example. Enclosure 1002 may further include a built inpressure reducer valve 2006 on the inlet pressure line. In an exemplaryembodiment, the pressure reducer valve 2006 (e.g., see also FIG. 13) mayreduce fluid/oil pressure from 5000 psi down to 50 psi before fluid/oilgoes through the enclosure 1002 and back to the equipment/engine.

In various embodiments, enclosure 1002 may include a 1-μm oil filter2014 (e.g., see also FIG. 13). Oil may flow through system 1000 in aparticular sequence to validate and ensure extended life oil beingcharacterized. In an embodiment, system 1000 may be configured in thefollowing order: wear metal sensor 1022, water sensor 1024, particlecount sensor 1026, viscosity sensor 1028, nano chip plug 2032, 1-μmfilter 2014, etc., as described in greater detail above with referenceto FIG. 13.

The above-described sequence of sensors may be important in determiningthe oil purity of the equipment since 1-μm filter 2014 may change theparticle count and moisture content in the oil. System 1000 mayextrapolate the wear metals, water, particle count, viscosity, and otherparameters before the oil crosses 1-μm filter 2014. The ability ofsystem 1000 to calculate a difference between the readings before andafter 1-μm filter 2014 may allow for accurate oil quality measurementand oil life predictive calculations. Since these readings may be onboth sides of the 1-μm filter 2014 (e.g., see FIG. 13 and relateddiscussion), a true reading of the oil and equipment condition may berealized. Taking readings in this order, on both sides of the 1-μmfilter (e.g., see FIG. 13), may thus further improve predictability ofthe lifecycle of the oil and equipment condition.

In exemplary embodiments, system 1000 may further include a nodeenclosure 1004 connected to enclosure 1002 as illustrated, for example,in FIG. 15B. Node enclosure 1004 may be a 12 inch×12 inch×6 inchweatherproof enclosure with an antenna 1008 for communicating datathrough one or more data communication connections including: LAN/WANconnection, which may be encrypted or unencrypted, via cellular,satellite, Wi-Fi, Bluetooth, Ethernet (RJ-45) connections, etc. Othersuitable dimensions for enclosure 1002 may be employed in alternativeembodiments. Node enclosure 1004 may track a plurality of inputs (e.g.,up to six different data inputs in one embodiment) into one useraccount. Each data point may relate to a separate enclosure 1002 thatmay be hard-wired back to the node enclosure 1004.

System 1000 may be piggy backed together with other systems 1000 for upto 36 different systems 1000 and may be routed back into one connectionat the node enclosure 1004. This particular configuration may allow forsystem 1000 to only have one communication node for multiple enclosures1002/systems 1000, providing great cost benefits to the consumer, andallowing for easier and cleaner installation of the system 1000. Nodeenclosure 1004 may further include connections to transmit collecteddata including video, audio, or sample data collected by system 1000.Data may be transmitted via a connection for satellite/Wi-Fi/cell towerantenna 1008 and a power port and/or Ethernet/HDMI (High DefinitionVideo Device Interface) port 1010.

Node enclosure 1004 may be outfitted with a rugged node 1006 for customprogramming of algorithms to compute and process sensor inputs and torelay crucial notification abilities via text, email, etc. The customprogramming may include computer program instructions related to oilanalysis processing and readings for the following: specific wearmetals, moisture levels, particulate counts, viscosity, TAN, TBN,Nitration, Sulfation, Foreign Oils, Solvents, Glycol, Soot, DissolvedGases, and/or Oil Additive Depletion (Zn, Mo, Pd, Ca, Mg, Ba, Na). Thecustom programming may also cause the system to send instantnotifications to a user the moment critical levels are reached asestablished by user-determined preferences or as determined by the NIST(National Institute of Standards and Technology) oil analysis standardsif there are no user-determined preferences are not programmed into thenode 1006. The software may have a predictive ability built into thedesign of the node 1006 that may notify users of upcoming preventivemaintenance.

In various embodiments, networking capabilities of the system 1000 maybe extended due to the ability of system 1000 to piggyback enclosures1002 together. Networking features include: (i) daisy chaining aplurality (e.g., 36 in one embodiment) of enclosures 1002 going to onenode enclosure 1004; or (ii) wiring a plurality of (e.g., 36 in oneembodiment) enclosures 1002 into the node enclosure 1004 directly. Oncethese multiple enclosures 1002 are transmitting data into the nodeenclosures 1004, system 1000 may combine an unlimited number of datapoints into one user account that may be accessible by the user on a24×7 basis via any Internet connected device. This may afford the userfull control over the monitoring and maintenance of equipment/enginethat is connected to disclosed embodiment systems.

In an exemplary embodiment, fluid/oil may be re-routed from an engine orother equipment through disclosed systems described, and back to theengine/equipment. Once fluid/oil is flowing through the system, wearmetals, moisture levels, particulate counts, viscosity, TAN, TBN,Nitration, Sulfation, Foreign Oils, Solvents, Glycol, Soot, DissolvedGases, and/or Oil Additive Depletion (Zn, Mo, Pd, Ca, Mg, Ba, Na),and/or oil temperature reporting may be tested and logged periodically(e.g., every millisecond, every second, every 2 seconds, etc., accordingto embodiments). In some embodiments, an additional sensor may be addedfor emissions monitoring. Each different measurement may be taken via aspecific sensor for each analysis data point. Data may be collected intocontroller 1012 built into the enclosure 1002 described herein.Controller 1012 may transmit the data to node 1006. In exemplaryembodiments, node 1006 may be a small Linux based computer (or otherwiseprogrammable device). Node 1006 may be programmed with custom algorithmsto compute and process sensor inputs from the controller 1012, and torelay notifications. Node 1006 may then transmit the data through thebest available method: LAN/WAN connection, which may be encrypted orunencrypted, via cellular, satellite, Wi-Fi, Bluetooth, Ethernet (RJ-45)connections, etc.

Once this data is transmitted, it may be stored in a storage device oron a cloud computing platform and the data may be readily available forthe user to access from a computer, tablet, smart phone, etc. If anInternet signal drops, node 1006 may rely on a storage device (e.g., a60 gigabyte hard drive in one embodiment) that may store the informationuntil the Internet signal is restored. Once an Internet connection isrestored, node 1006 may automatically dump all of the data to a cloudbased storage platform. If there is critical information gathered fromthe system, the user may be notified via text, email, etc. A user maylog into their account with custom designed dashboards so they can seeall equipment and data points being monitored. Custom dashboards andalerts may be determined by the user to meet his/her individual needs.Alerts may be sent to the user via email, text message, etc.,automatically from the system based on algorithms that may be programmedfor specific types of measurements. The online dashboard may beweb-based and may be accessed from any device that has an Internetconnection. The dashboard may be reactive and configured toautomatically collapse and stack the data to a tablet and/or cell phoneview, for example, if the user is not accessing the system from acomputer/web browser.

Once system 1000 is installed and parameters have been programmed intonode 1006, a user may be able to interact with the system withoutrecourse to consultation from a supplier/provider for management andmaintenance of his/her equipment. In some embodiments, system 1000described herein may be used to perform real-time oil analysis samplingfrom multiple pieces of equipment. Sampling from multiple pieces ofequipment may be accomplished through customized multi-flow controlvalves that may allow oil to be brought in from multiple pieces ofequipment using the same type of oil. In embodiments, the pieces ofequipment may be located in the same vicinity as each other and system1000. In other embodiments, the pieces of equipment may be locatedfurther away/remotely from each other and from system 1000.

Multi-flow control valves may be controlled via a graphical userinterface (GUI) having custom designed dashboards. Multi-flow controlvalves may be configured as manifold-control valve connections. Flowcontrol valves may be inlet multi-flow control valves and/or outletmulti-flow control valves. System 1000 may include an inlet multi-flowcontrol valve programmed to allow oil to flow into an enclosure 1002from only one engine at a time via an inlet valve described herein.System 1000 may further include an outlet multi-flow control valveprogrammed to allow for the oil to be returned to the same engine fromwhich it was pulled via an outlet valve described herein and a returnline going back to the same equipment.

In an exemplary embodiment, once an analysis is made over apredetermined period of time (e.g., 10 to 60 minutes according to anexample embodiment of the present disclosure), the inlet valve mayswitch off, at which time the system may be programmed to notify anothervalve to open for a next piece of equipment that may have beenprogrammed in a sampling sequence. In some embodiments, a predetermineddelay (e.g., 60 to 180 seconds in one embodiment) may occur between theopening of a new valve and for the system 1000 to start taking readingsto clean out the lines feeding the system 1000. In other embodiments,this sequence of changing between different pieces of equipment may beprogrammed from every few seconds, every few minutes, once per hour,etc., per piece of equipment, depending on a customer's needs.

In exemplary embodiments, once system 1000 is taking readings from eachdifferent engine/equipment, it may be configured to then run comparativealgorithms in a separate custom designed dashboard described herein, andto thereby perform comparative analysis of oils from different equipmentto determine which engines may be running most efficiently and whichengines may be in need of extra attention, modifications, and/orservice. Detailed reporting may allow customers to pinpoint any problemswith efficiency in different pieces of equipment and solve any problemsthat they may not have known existed. Further, this reporting may alsoallow customers to determine for themselves which engines may be runningmost efficiently and which engines may need to be replaced.

FIG. 16 is a schematic of a fluid analysis and monitoring system 5000 e,according to an example embodiment of the present disclosure. System5000 e is similar to system 5000 d of FIG. 5, but is further configuredto include analytical systems 400 a and 400 b. Analytical systems 400 aand 400 b may communicate with user devices 307 through one or morenetworks 305, according to an example embodiment of the presentdisclosure, as described in greater detail below with reference to FIGS.17 and 18. Analytical systems 400 a and 400 b further include commandand control systems 406 a and 406 b and databases 402 a and 402 b, asdescribed in greater detail below.

FIG. 17 is a schematic of a fluid analysis system 100 a, according to anexample embodiment of the present disclosure. Fluid analysis system 100a may include an enclosure 300 a having a cooling system 302attached/coupled to a sampling system 304, and an analytical system 400a coupled to the sampling system 304. Fluid may be routed out from afluid source 200 and into cooling system 302 (e.g., shown via arrow A)for cooling the fluid prior to routing the fluid into sampling system304 (shown via arrow B). Sampling system 304 may collect data from thefluid. In an exemplary embodiment, data may include spectral data of afluid sample obtained via spectroscopy. Other forms of data/informationmay also be obtained from the fluid sample, as described in greaterdetail below. Sampling system 304 may then process and transmit the datato analytical system 400 a.

Analytical system 400 a may be directly connected to sampling system 304as an external storage device. In further embodiments, analytical system400 a may be located onboard a ship or on other remote structure.Sampling system 304 may provide data to analytical system 400 a througha direct wired or wireless connection (e.g., shown by double arrow C),that provides a bi-directional communication link.

In a further embodiment, an analytical system 400 b may be provided as aremote device that is accessible through one or more networks 305.Network 305 may be a local area network (LAN), a wide area network(WAN), or may be the Internet. In further embodiments, analytical system400 b may be implemented as a software module running on a remotedevice, on a server, or on a cloud based computing platform.Bi-directional wireless links C may also be provided to connectanalytical system 400 b with network 305, to connect network 305 withuser devices 307, to connect user devices 307 with analytical system 400a, and to connect sampling system 304 with network 305.

In further embodiments, sampling system 304 may provide data toanalytical system 400 b, for example, via network 305 through an uplinkto a LAN/WAN connection, which may be encrypted or unencrypted, viacellular, satellite, Wi-Fi, Bluetooth, Ethernet (RJ-45) connections,etc.

A user interface may be provided on one or more user devices 307. Userdevices 307 may communicate directly with analytical system 400 a via awired or wireless connection. User devices 307 may also communicateindirectly with analytical system 400 b via network 305. A user mayaccess and/or modify analytical systems 400 a and/or 400 b via a webapplication, for example, running on a computing device 307 (e.g., adesktop computer, portable device, etc.) through any type of encryptedor unencrypted connection, as described above. Once processing ofmeasured data by analytical systems 400 a and/or 400 b is complete,fluid may be returned from sampling system 304 to cooling system 302(shown via arrow D) and eventually back to fluid source 200 (shown viaarrow E). In other embodiments, if the fluid does not require cooling,fluid may be routed directly (not shown in FIG. 17) from fluid source200 into sampling system 304 and back.

Analytical systems 400 a and 400 b may include respective command andcontrol systems 406 a and 406 b, as shown in FIG. 17. Command andcontrol systems 406 a and 406 b may be configured to receive data fromfluid sampling system 304 and to store such data in respective databases402 a and 402 b of analytical systems 400 a and 400 b. Command andcontrol systems 406 a and 406 b may compare received data topreviously-determined data for particular fluids stored in respectivedatabases 402 a and 402 b. Based on the comparison, command and controlsystems 406 a and 406 b may identify correlations between received dataand previously stored data for particular fluids. The identifiedcorrelations may then be used to identify conditions of the fluid. Theidentified conditions of the fluid may include chemical composition,presence of impurities, debris, wear metals, etc.

Command and control systems 406 a and 406 b may be configured as hostedsoftware systems that may receive data collected by sampling system 304for the submitted sample of the fluid. Command and control systems 406 aand 406 b may then process such data through a set of existing machinelearning models to generate a predictive analysis of properties andconditions of the fluid. Machine learning models may be configured totarget any type of fluid to be analyzed. The resulting output of thesample analysis will generally be dependent on the fluid submitted, thenetworks processed (i.e., in the case of neural network models), and thestatistical percentage accuracy of the given machine learning model. Invarious embodiments, a user may update existing machine learning modelsor build new machine learning models (via “training”) if received datadoes not correspond to any of a set of existing machine learning models.In various embodiments, command and control systems 406 a and/or 406 bmay then deploy updated and/or new machine learning models back to thefluid analysis system 100 a, including the sampling system 304. Invarious embodiments, command and control systems 406 a and 406 b mayalso be configured to manage a user/client's security credentials andcustomized settings.

Database 402 a may be located on a computer readable storage device suchas a non-transitory memory device. For example, database 402 a may belocated on a read-only-memory (ROM) device. Database 402 a may also bestored on a volatile storage device such as a random-access-memory (RAM)device. Database 402 b may be located on an external device that isaccessible via network 305. For example, database 402 b may be locatedon a server or on a cloud based computing platform.

Databases 402 a and 402 b may be used to collect and store data relatingto different types of fluids (e.g., types of oil and water) and theirconditions. Fluids may include, but are not limited to, any type ofindustrial fluids or liquids, such as coolants, waste water, etc. Oilsmay include any type of oil, including but not limited to, very lightoils such as jet fuels and gasoline, light oils such as diesel, No. 2fuel oil, and light crudes, medium oils such as most crude oils, andheavy oils such as heavy crude oils, No. 6 fuel oil, and Bunker C. Thedifferent “conditions” of fluid/oil samples may describe compositionscontaining various fluids, impurities, wear metals, additives, water,etc. Fluid “conditions” may also describe various properties such asviscosity, total acid number (TAN), total base number (TBN), andparticle counts. In exemplary embodiments, existing data in databases402 a and 402 b may include spectroscopic information regarding themolecular content or makeup of different types of fluid.

In some embodiments, default fluid sensor dashboards may also beprovided for each installation site at time of installation of system100 a. Such dashboards may be provided on a graphical user interface(GUI) (not shown) of a user device 307. Each approved user may have anability to customize or alter these dashboards as desired. In exemplaryembodiments, software associated with the dashboards may providereal-time monitoring and graphical updates at predetermined data rates.For example, graphical updates may be provided each time data isdetermined to have changed. In other embodiments data may be updated anupdate rate not to exceed 1 second, 10 seconds, 100 seconds, 180seconds, etc.

In other embodiments, real-time display inclusive of graphicaldepictions may be capable of being continuously updated while data isbeing viewed. Data screens and access capabilities may be automaticallyresized to fit a viewing area of user devices 307 used to access thedashboards. Data acquisition and analytics in the dashboards mayinclude, but is not limited to, the following capabilities: analyticalcomparatives and real-time updates (between sampling system 304 andanalytical systems 400 a and 400 b); predictive oil changing comparativeanalysis, chronograph data, financial comparative data; data regardingwear metals, particulate counts, viscosity, TAN, TBN, Nitration,Sulfation, Foreign Oils, Solvents, Glycol, Soot, Dissolved Gases, and/orOil Additive Depletion (Zn, Mo, Pd, Ca, Mg, Ba, Na), area plots(illustrating how a customer may view a layout of the system 100 a); andnotifications suggesting that required maintenance is pending.

In an embodiment, enclosure 300 a may be a ruggedized andwater-resistant case. For example, enclosure 300 a may be mounted viascrews and/or bolts onto a flat surface using, for example, rubberbushings/shock absorbers to minimize vibrational noise. Enclosure 300 amay also include other suitable configurations for securely holding bothcooling system 302 and sampling system 304.

Disclosed embodiments may be designed using a “plug and play”philosophy. Each component of fluid analysis system 100 a may be easilyplugged/snapped to other components of fluid analysis system 100 a viafluidic connectors (306 a, 306 b, 306 c, and 306 d) and via anelectrical wiring connections W, as illustrated in FIG. 17. For example,cooling system 302 may or may not be plugged into sampling system 304depending on the temperature of the fluid. In exemplary embodiments,connectors (306 a, 306 b, 306 c, and 306 d) may be fluidic Eaton STC®“snap” connectors allowing for fluid to be routed into and out ofsampling system 304 from cooling system 302.

FIG. 18 is a schematic of a fluid analysis system 100 b, according to anexample embodiment of the present disclosure. Fluid analysis system 100b has features that are similar to those of fluid analysis system 100 ashown in FIG. 17. In this embodiment, cooling system 302 may beinstalled separately from and/or externally to enclosure 300 b of fluidanalysis system 100 b having sampling system 304. Cooling system 302 maybe fluidically coupled to enclosure 300 b/sampling system 304 viaconnectors 306 a and 306 b. In this example, fluid connectors 306 a and306 b are shown as external connectors to sampling system 304. In otherembodiments, such as shown in FIG. 17, connectors 306 a to 306 d may beprovided as internal connectors to respective systems 302 and 304.Cooling system 302 may also be electrically connected to enclosure 300b/sampling system 304 via wiring connections W.

The configuration of system 100 b, illustrated in FIG. 18, providesgreater flexibility by allowing fluid analysis system 100 b to bedeployed with or without a cooling system 302, as needed to suit auser's needs. In an exemplary embodiment, cooling system 302 may only becoupled to the enclosure 300 b/sampling system 304 if the fluid beingrouted through the system 100 b requires cooling. In this embodiment,enclosure 300 b having sampling system 304 may include a smaller sizedcase than the embodiment of enclosure 300 a having both cooling system302 and sampling system 304, as illustrated in FIG. 17. Other details ofFIG. 18 not specifically mentioned (e.g., analytical systems 400 a and400 b, network 305, user devices 307, pathways A to E, etc.) areessentially similar to those described above with respect to FIG. 17.

FIG. 19 is a schematic of a fluid analysis with an enclosure 300 a and acooling system 302 a, according to an example embodiment of the presentdisclosure. As described herein, cooling system 302 a (e.g., see alsoFIGS. 17 and 18) may be a separately pluggable piece that may be coupledto sampling system 304 if and when a fluid requires cooling, asillustrated in FIG. 18 for system 100 b. Alternatively, cooling system302 a may come pre-installed within an enclosure 300 a along withsampling system 304, as illustrated in FIG. 17.

Cooling system 302 a may be used to control, filter, and cool fluid(e.g. oil, water, etc.) to be sampled from a fluid source 200 (e.g., seeFIGS. 17 and 18). In an exemplary embodiment, fluid may be oil that isrouted from a fluid/oil source 200, such as an engine, via pressure fromsource 200 that forces the oil into cooling system 302 a (shown viaarrow A in FIGS. 17 to 19). Fitting 316 a may be used to connect an oilline from a high pressure line from source 200 to cooling system 302 a.In some embodiments, fitting 316 a may be a connector (e.g., a connectorsimilar to connectors 306 a to 306 d of FIGS. 17 and 18) such as anEaton STC® “snap” connector.

In other embodiments, fitting 316 a may be configured to connect the anysize oil line source 200 to a cooling system 302 a. For example, fitting316 a may be a 1/16, ⅛, ¼, or ½″ Female Iron (or International) Pipe(FIP) fitting. Cooling system 302 a may include a valve 314 a connectedto source valve manifold assembly 360 and connected to various wiringconnections W (explicit wiring connections not shown in FIG. 19). Valve314 a may be used to control flow of oil into cooling system 302 a. Insome embodiments, valve 314 a may be an electromechanical singledirection solenoid valve. In an exemplary embodiment, valve 314 a may bean AS Series Valve offered by Gems™ Sensors & Controls. Source manifoldassembly 360 may be a Manifold Assembly offered by Gems™ Sensors &Controls. Valves 314 a and 314 b may be controlled via connections(e.g., via electrical connectors 390 a and 390 b, respectively) to acontroller 309 a located in the cooling system 302 a, as shown in FIG.19, and/or connected to controller 332 located in sampling system 304,as shown, for example, in FIG. 23. Controller 309 a and/or controller332 may send a signal to cause valve 314 a to open and close as neededto allow fluid/oil into the cooling system 302 a.

As described below, controller 309 a or 332 (e.g., see FIG. 23) maymonitor current drawn by solenoid valves to detect system failures. Inthis regard, as valves driven by solenoids begin to fail, the solenoidsdraw more electrical current to perform the same functions. For example,with a sticky valve, an electrical short, etc., higher electricalcurrent may be drawn by a failing valve.

As shown in FIG. 19, fluid/oil may be routed from the source manifoldassembly 360 through a filter connection 318 and into a filter 320located outside cooling system 302 a. In other embodiments, filter 320may be located inside cooling system 302 a. Filter connection 318 andfilter 320 may be used to prevent debris in oil from entering coolingsystem 302 a and damaging cooling system 302 a and/or eventuallyentering sampling system 304. Fluid/oil may then be routed into apressure reducer regulator valve with a pressure sensor 308.

Pressure reducer valve 308 may include two inputs and one output asshown, for example, in FIG. 19. In an exemplary embodiment, pressurereducer valve 308 may be a BB-3 series stainless steel back-pressureregulator offered by Tescom™. Other pressure reducer values may be usedin other embodiments. In various embodiments, pressure reducer valve 308may reduce the pressure from dangerously high pressures (e.g.,pressures >50 psi) in source 200 to between approximately 1 and 50 psi(depending on fluid type). Once pressure of the fluid/oil is reduced toa safe value, fluid/oil may be routed into a cooler/radiator 324 andthen to a temperature sensor 310 and a 2-way solenoid valve 312. In someembodiments, cooler 324 may either be a simple radiant heat sink or afluid cooler system. In an exemplary embodiment, cooler 324 may be aMMOC-10 Universal 10-Row Oil Cooler offered by Mishimoto™. Other coolersmay be used in further embodiments.

In an exemplary embodiment, if the temperature sensor 310 detects atemperature of the fluid/oil that is greater than a predetermined value,say 40° C., controller 309 a of FIG. 19 or controller 332 of FIG. 23 mayswitch valve 312 by applying a control signal to an electrical connector(not shown) and route the fluid/oil out of cooling system 302 a viaconnector 306 a and into sampling system 304 (e.g., see FIGS. 17 and 18)(via arrow B), as shown in FIG. 19. However, if temperature sensor 310detects that the fluid/oil is at a temperature that is above apredetermined value, say 40° C., it may route the fluid/oil back intopressure reducer valve 308 and into cooler 324 via valve 312 until thefluid/oil reaches the desired temperature (e.g., a temperature less thanor equal to 40° C.). This temperature is relevant because it is relatedto measurement of a viscosity of the fluid/oil, as described in greaterdetail below.

The viscosity of a lubricating fluid/oil may be measured either based onits kinematic viscosity, acoustic viscosity, or its absolute (dynamic)viscosity. Kinematic viscosity is defined as its resistance to flow andshear due to gravity at a given temperature. However, simply stating aviscosity of a fluid/oil is meaningless unless the temperature at whichthe viscosity was measured is specified. For most industrial oils, it iscommon to measure kinematic viscosity at 40° C. because this is thebasis for the ISO viscosity grading system (ISO 3448).

In an exemplary embodiment, an acoustic viscosity sensor may beemployed. An acoustic viscometer may employ a piezoelectric sensorhaving distinct input and output ports for differential measurements.Acoustic viscometers may also include a multi-reflective acoustic wavedevice blends the features of resonators and delay lines to offer a widedynamic range (air to several thousand centipoise) in a single sensor.Aside from the atomic-scale vibration of the acoustic viscometersurface, such sensors have no other moving parts that may wear or breakover time. In addition, the high frequency of the vibration, which maybe up to several million vibrations per second, is independent of flowconditions of the fluid and also immune to environmental vibrationeffects which may be found is hostile environment such as an engineroom.

In various embodiments, fan 370 may be installed within cooling system302 a and may be turned on as needed (e.g. the fan may be turned on whenthe temperature of the oil is >40° C.) to assist cooler 324 in coolingthe fluid/oil based on the temperature of the fluid/oil and based on aradiant air temperature. Fan 370 may be controlled via the controller309 a of cooling system 302 a (e.g., see FIG. 19) or via controller 332of sampling system 304 (e.g., see FIG. 23) through electrical connection375. Fan 370 may generate a flow of air along an airflow path 380.

Wiring connections W may be used to connect various electricalconnections of cooling system 302 a to sampling system 304 (e.g., seeFIGS. 17 and 18), as shown in FIG. 19. Wiring connections W may includean electrical ground connection to force the electrical potential ofcooling system 302 a to coincide with a ground potential. If electricalconnections W does not include a ground connection, a separate groundconnection G may be provided as illustrated, for example, in FIG. 19. Asmentioned above, electrical connection 390 a, 390 b, etc., may providecontrol signals to various control valves (e.g., valves 314 a and 314 b,etc.).

As shown in FIGS. 17 to 19, once the fluid/oil is adequately sampled bysampling system 304, oil may be routed back from sampling system 304(e.g., see FIGS. 17 and 18) to cooling system 302 a (shown via arrow Din FIGS. 17 to 19), via connector 306 b. To facilitate this return,cooling system 302 a may include an air valve 322 that may be opened asneeded to purge air from the line and to accelerate return of oil ifthere is little or no pressure to push/drain the oil back into coolingsystem 302 a from sampling system 304 through connector 306 b. Fluid/oilmay then be routed out of cooling system 302 a and back to source 200(e.g., shown by arrow E in FIGS. 17 to 19) via a similar fitting-valve316 b/return-valve 314 b manifold assembly 362 connection that allowsentry of oil into cooling system 302 a, as illustrated in FIG. 19.Return manifold assembly 362 may be a Manifold Assemblies offered byGems™ Sensors & Controls. Other return manifold assemblies may beprovided in further embodiments.

FIG. 20 is a schematic of a fluid analysis with an enclosure 300 a and acooling system 302 b, according to an example embodiment of the presentdisclosure. Cooling system 302 b has features similar to those ofcooling system 302 a shown in FIG. 19. In this embodiment, coolingsystem 302 b may include a pump 326 connected to a fluid source 200containing fluids having little or no pressure. Pump 326 may provideadditional pressure/movement for these fluids to be routed into coolingsystem 302 b and eventually into sampling system 304 (e.g., see FIGS. 17and 18). In an exemplary embodiment, fluid/oil may be routed from source200 into pump 326. Pump 326 may then pump fluid/oil into cooling system302 b (shown via arrow A).

The behavior of cooling system 302 b, illustrated in FIG. 20, is similarto the behavior of cooling system 302 a illustrated in FIG. 19. In thisregard, in cooling system 302 b, fluid/oil flows along path A throughconnector 316 a and through valve 314 a into source manifold assembly360. From there, fluid/oil may be routed into filter 320 via filterconnection 318. Fluid/oil may then flow into pressure reducer valve 308,cooler 324, temperature sensor 310, and through 2-way solenoid valve312. Fluid/oil may then be provided to sampling system 304 (e.g., seeFIGS. 17 and 18) by flowing through connector 306 a and into samplingsystem 304 along path B. Fluid/oil may then flow back to cooling system302 b through connector 306 b along path D. Fluid/oil may then flow intoreturn manifold 362, and back to source 200 along path E, by flowingthrough valve 314 b and connector 316 b, as described above withreference to FIG. 19.

Pump 326 may include electrical connections to sampling system 304 viawiring connections W. Such electrical connections may be similar toelectrical connections between cooling system 302 and sampling system304, via wiring connections W, described above with reference to FIGS.17 and 18. Wiring connections W may include an electrical power supplythat supplies electrical power to pump 326. As mentioned above, aseparate electrical power connection P may be provided to pump 326 if anelectrical power connection is not provided by wiring connections W.

Pump 326 may be initialized via connections to controller 309 a (e.g.,see FIG. 20) located in the cooling system 302 b, and/or connections tocontroller 332 (e.g., see FIG. 23) located in sampling system 304. Invarious embodiments, controller 309 a in cooling system 302 b and/orcontroller 332 in sampling system 304 may shut pump 326 down oncesampling is complete. Controller 309 a and/or controller 332 may thenopen air valve 322 as needed to purge air from the line and to helpaccelerate return of fluid/oil if there is little or no pressureotherwise provided by the source 200 to push/drain the fluid/oil backinto cooling system 302 b. Features not specifically described (e.g.,electrical connectors 375, 390 a, 390 b, fan 370, and airflow path 380),are similar to those described above with reference to FIG. 19.

FIG. 21 is a schematic of a fluid analysis with an enclosure 300 a and acooling system 302 c, according to an example embodiment of the presentdisclosure. In this embodiment, cooling system 302 c is shown as havingconnections to multiple fluid sources 200 a and 200 b for cooling androuting fluid into sampling system 304 (e.g., see FIGS. 17 and 18). Inan embodiment, fluid from a single fluid source (e.g., fluid source 200a or 200 b) may be cooled and sampled at a time. In further embodiments,cooling system 302 c may be simultaneously connected to two fluidsources 200 a and 200 b via multiple fittings 316 a to 316 d andcorresponding respective multiple valves 314 a to 314 d attached to eachof the input/inlet and return/outlet sides, each of which may becontrolled independently of the others based on the fluid/oil to besampled. Valves 314 a to 314 d may be actuated by control signalsprovided from controller 309 b via electrical connectors 390 a to 390 d,respectively.

As shown in FIG. 21, fittings 316 a and 316 b may be connected tosources 200 a and 200 b, respectively. Fittings 316 a and 316 b may beconnected to valves 314 a and 314 b, respectively, which may in turn beconnected to single source manifold assembly 360. Similarly, valves 314c and 314 d may be connected to single return manifold assembly 362.Further, fittings 316 c and 316 d may be connected to valves 314 c and314 d, respectively. In turn, valves 314 c and 314 d may be connected asreturn valves to sources 200 a and 200 b, respectively.

Each valve 314 a to 314 d may be controlled via connections tocontroller 309 b located in the cooling system 302, as shown in FIG. 21,and/or may be controlled via connections to controller 332 of samplingsystem 304, as shown in FIG. 23. Controller 309 b (e.g., see FIG. 21)and/or controller 332 (e.g., see FIG. 23) may send signals to anappropriate valve 314 a and/or 314 b associated with source manifoldassembly 360 causing the valve in question to open, thus allowing flowof fluid/oil into cooling system 302 c from sources 200 a and/or 200 b,respectively. Similarly, controller 309 b and/or controller 332 (e.g.,see FIG. 23) may send signals to an appropriate valve 314 c and/or 314 dassociated with return manifold assembly 362 causing the valve inquestion to open, thus allowing flow of fluid/oil out of cooling system302 c and back to sources 200 a and 200 b, respectively. Some of valves314 a to 314 d may be opened while others of valves 314 a to 314 d maybe closed, depending on the fluid/oil sample and depending on which oneor both of sources 200 a and 200 b are selected for sampling.

For example, opening valve 314 a allows fluid/oil to flow from source200 a along path A1 through connector 316 a and into cooling system 302c via source manifold assembly 360. Similarly, opening valve 314 ballows fluid/oil to flow from source 200 b along path A2 throughconnector 316 b and into cooling system 302 c via source manifoldassembly 360. Opening valve 314 c allows fluid/oil to flow out fromreturn manifold assembly 362 through connector 316 c and to return fromcooling system 302 c to source 200 a via path E1. Similarly, openingvalve 314 d allows fluid/oil to flow out from return manifold assembly362 through connector 316 d and to return from cooling system 302 c tosource 200 b via path E2.

Other details of FIG. 21 not specifically mentioned (e.g., connectors306 a and 306 b), pressure reducer valve 308, temperature sensor 310,2-way solenoid valve 312, filter connection 318, oil filter 320, airvalve 322, cooler 324, fan 370, airflow path 380, paths B and D, wiringconnections W, ground connection G, electrical connectors 375, etc. areessentially similar to those described above with respect to FIG. 20.

FIG. 22 is a schematic of a fluid analysis system with an enclosure 300a and a cooling system 302 d, according to an example embodiment of thepresent disclosure. Cooling system 302 d has features similar to thoseof cooling system 302 c shown in FIG. 21. In this embodiment, coolingsystem 302 d may include a pump 326 connected to multiple fluid sources200 a and 200 b containing fluids having little or no pressure. Asshown, source manifold assembly 360 may be located externally to coolingsystem 302 d, thereby preventing duplicative valve systems on the inputline to cooling system 302 d, such as valves 314 a and 314 b of coolingsystem 302 b shown in FIG. 21. Further, providing the source manifoldassembly 360 as a device that is external to cooling system 302 d allowsfluid/oil from multiple sources (e.g., engines) 200 a and 200 b to beprovided into a single line prior to being routed into pump 326, thuseliminating the need for multiple pumps 326, as illustrated in FIG. 22and described in greater detail below.

As shown in FIG. 22, fluid/oil may be routed from two sources 200 a and200 b into fittings 316 a and 316 b, respectively, along paths A1 andA2. Flow of fluid/oil through fittings 316 a and 316 b may be controlledby valves 314 a and 314 b, respectively, which are attached to andcontrol the flow of fluid/oil into source manifold assembly 360.Fluid/oil may flow out of source valve manifold 360 through fitting 316e along path A3 and into pump 326 through fitting 316 f. Pump 326 mayprovide pressure to fluid/oil and move fluid/oil out of pump 326 throughfitting 316 g along path A4. Fluid/oil may then propagate into coolingsystem 302 d though fitting 316 h.

As with the behavior of cooling system 302 c, illustrated in FIG. 21,fluid/oil may then flow into filter 320 via filter connection 318.Fluid/oil may then flow into pressure reducer valve 308, cooler 324,temperature sensor 310, and through 2-way solenoid valve 312. Fluid/oilmay then be provided to sampling system 304 (e.g., see FIGS. 17 and 18)by flowing through connector 306 a and into sampling system 304 alongpath B. Fluid/oil may then flow back to cooling system 302 d throughconnector 306 b along path D. Fluid/oil may then flow into returnmanifold 362. Opening valve 314 c allows fluid/oil to flow out fromreturn manifold assembly 362 through connector 316 c and to return tosource 200 a via path E1. Similarly, opening valve 314 d allowsfluid/oil to flow out from return manifold assembly 362 throughconnector 316 d and to return to source 200 b via path E2.

Each of valves 314 a to 314 d may be controlled via connections (notshown for simplicity of illustration) to controller 309 b located in thecooling system 302 d (e.g., see FIGS. 21 and 22) and/or controller 332located sampling system 304 (e.g., see FIG. 23). Controllers 309 band/or 332 may send a signal to an appropriate valve 314 a and/or 314 bon the source manifold assembly 360 and/or valves 314 c and 314 d onreturn manifold assembly 362 to control the flow of fluid/oil, asdescribed above with reference to FIG. 21. Other details of FIG. 22 notspecifically mentioned (e.g., open air valve 322, fan 370, airflow path380, wiring connections W, ground connection G, power connection P,electrical connection 375, etc.) are essentially similar to thosedescribed above with respect to FIGS. 19 to 21.

FIG. 23 is a schematic of a sampling system 304, according to an exampleembodiment of the present disclosure. As shown, arrow B represents fluidbeing routed into sampling system 304 through connector 306 c fromcooling system 302 and/or from fluid source 200 as shown, for example,in FIGS. 17 and 18. Arrow D represents fluid being returned throughconnector 306 d to cooling system 302 and/or to fluid source 200 (e.g.,see FIGS. 17 and 18) after sampling has been performed on a fluid sampleby sampling system 304. Arrow W represents wiring connections betweencomponents of sampling system 304, and between sampling system 304 andcooling system 302 (e.g., see FIGS. 17, 18 and 23).

Sampling system 304 may include at least one removable and replaceablesub-sampling system 330. In further embodiments, sampling system 304 mayinclude a plurality of sub-sampling systems 330, 330 a, 330 b, etc.Plurality of sub-sampling systems 330, 330 a, 330 b, etc., may bestacked in a daisy-chain configuration and may be electrically connectedvia wiring connections W and fluidically connected to one another viaconnectors (e.g., connectors 306 e, 306 f, etc.), as described ingreater detail below with reference to FIGS. 24 to 29.

Reference characters 330, 330 a, 330 b, etc., are used for convenienceof illustration and description. There is no restriction, however, onthe ordering of identity of sub-sampling systems in sampling system 304of FIG. 23 or in the description of sub-sampling systems in FIGS. 24 to29. For example, sub-sampling systems (330), (330 a), (330 b), etc., mayall be the same, all different, or any combination of sub-samplingsystems described below with reference to FIGS. 24 to 29.

In various embodiments, connectors (e.g., connectors 306 e, 306 f,etc.), illustrated in FIGS. 24 to 29 may be Eaton STC® “snap” connectorsallowing fluid to be routed into and out of sub-sampling systems 330,330 a, 330 b, etc. In this regard, each sub-sampling system 330, 330 a,330 b, etc., may have a female input connector (on the top, asillustrated in FIGS. 24 to 29) and a male output connector (on thebottom, as illustrated in FIGS. 24 to 29), allowing each sub-samplingsystem 330, 330 a, 330 b, etc., to be stacked sequentially (e.g., seeFIG. 23) to satisfy fluid and target requirements. The types ofsub-sampling systems 330, 330 a, 330 b, etc., used within samplingsystem 304 may depend on the fluid and targeted identification criterianeeded.

In various embodiments, sampling system 304 may further includeconnections between input connector 306 c, input pressure reducer valve308 a (having pressure sensors/transducers), and input temperaturesensor 310 a. Sampling system 304 may further include connections to2-way solenoid valve 312 a that functions as a bypass valve (asdescribed in greater detail below), to at least one viscometer 328, andto controller 332, as described above with reference to FIGS. 19 to 22.Sampling system 304 may include several wiring connections W thatconnect controller 332 to each sub-sampling system 330, 330 a, 330 b,etc. (e.g., via daisy-chain configuration). Sampling system 304 mayfurther include connections between output connector 306 d and outputpressure reducer valve 308 b.

Wiring connections W may further provide electrical connections toviscometer 328, pressure reducer valves 308 a and 308 b, temperaturesensor 310 a, and 2-way solenoid valve 312 a. Wiring connections W mayfurther include a ribbon to an external connector that couples samplingsystem 304 to cooling system 302, as shown in FIGS. 17 and 18.Controller 332 may control the sampling system 304 and/or cooling system302, as described above with reference to FIGS. 19 to 22. Controller 332may further interact with analytical systems 400 a and/or 400 b, forexample, by submitting real-time data obtained from fluids being sampledto analytical systems 400 a and/or 400 b, as described above withreference to FIGS. 17 and 18.

Once fluid is routed into sampling system 304, 2-way solenoid valve 312a may divert the fluid back to cooling system 302 via a return line ifthe pressure and/or temperature of the fluid are too high or low (i.e.,if the pressure and/or temperature exceed respective predeterminedthreshold values). Pressure reducer valve 308 b may be located at anoutput/return line and pressure reducer valve 308 a may be located at aninput/source line. Pressure reducer valves 308 a and 308 b may be usedto generate pressure difference data. The generated pressure differencedata may be used to perform a pressure comparison between input andoutput pressures of the fluid to determine if a significant pressuredrop exists. A detected significant pressure drop may indicate apossible fluid leak.

The pressure comparison may be performed during sampling of the fluidsby allowing sub-sampling systems 330, 330 a, 330 b, etc., to equalize inpressure while data samples are generated. A change in pressure afterequalization (i.e. a significant pressure drop may imply the presence ofa leak within one or more of the sub-sampling systems 330, 330 a, 330 b,etc.). Further, a significant pressure drop may also be used to identifya fluidic leak at output pressure reducer valve 308 b. To determine ifoutput pressure reducer valve 308 b is leaking, a user may monitorelectrical current required to operate a solenoid associated with outputpressure reducer valve 308 b. As mentioned above, valves driven bysolenoids that are failing generally draw more electrical current toperform the same functions. Monitoring electrical current drawn bysolenoid valve lines, therefore, may provide self-diagnostic informationfor sampling system 304/fluid analysis systems 100 a and 100 b (e.g.,see FIGS. 17 and 18).

As shown, 2-way solenoid valve 312 a may divert fluid to viscometer 328if the pressure and/or temperature of the fluid are at an appropriatelevel (i.e., if the pressure and/or temperature are below respectivepredetermined threshold values). Viscometer 328 may be used to measureviscosity and flow parameters of the fluid. In an exemplary embodiment,viscometer may be a VISCOpro 2000 Process Viscometer offered by thePetroleum Analyzer Company, L.P. d/b/a PAC. Various alternativeviscometers may be also be use such a ViSmart™ acoustic viscometeroffered by BiODE, or a VTX423 “pinch” viscometer offered by TDCollaborative. Once the viscosity of the fluid is measured, fluid may berouted into sub-sampling systems 330, 330 a, 330 b, etc. In an exemplaryembodiment, fluid may be routed from viscometer 328 into sub-samplingsystems 330, 330 a, 330 b, etc., that may be stacked on top of oneanother, as illustrated in FIG. 23. As described in greater detail belowwith reference to FIGS. 24 to 29, fluid may be sampled by each ofsub-sampling systems 330, 330 a, 330 b, etc.

All components of sampling system 304 may be connected to controller 332via wiring connections W, as illustrated in FIG. 23. In an exemplaryembodiment, controller 332 may be an ARM (Acorn RISC Machine/AdvancedRISC Machine) based system with a custom shield (not shown in FIG. 23)for connecting to cooling system 302 (e.g., see FIGS. 17 to 22),sub-sampling systems 330, 300 a, 300 b, etc., and/or to other componentsof cooling 302 and sampling 304 systems. In exemplary embodiments,controller 332 may include an RJ45 (CATS/6 Ethernet connection 334), anSMA (SubMiniature version A) connection 336 for an antenna or an antennadongle, and a power connector 338, as illustrated, for example, in FIG.23. Controller 332 may also include connections including, for example,Universal Serial Bus (USB), HDMI, and Bluetooth connections, and may bepowered via a Mini-USB connection. In exemplary embodiments, controller332 may be the Raspberry Pi 3 Model B, Raspberry Pi Zero, or RaspberryPi 1 Model A+. In other embodiments, controller 332 may be the MojoBoard V3 offered by Embedded Micro—an FPGA (Field Programmable GateArray) with multiple pre-made shields. In further embodiments, any othersuitable controller 332 may be used.

Shields used to connect controller 332 to other components of samplingsystem 304 and/or cooling system 302 may include a Servo Shield (usedfor connecting to servos/solenoids on valves), Proto Shield (used forprototyping), IO Shield (used for displaying output), buttons for input,and switches for configuration options, and/or stackable headers (usedto stack shields offered by Embedded Micro). In some embodiments,controller 332 may be placed within its own enclosure (not shown in FIG.23) separate from an enclosure of sampling system 304 to protectcontroller 332 in case of a catastrophic fluid failure/leak withinsampling system 304. In other embodiments, controllers 309 a or 309 bmay be included in cooling systems 302 a to 302 d, as described ingreater detail above with respect to FIGS. 19 to 22.

In exemplary embodiments, controller 332 may include customized softwareto assist sampling system 304 in performing analysis of fluid and toassist in sending/receiving real-time data regarding the fluid toanalytical systems 400 a and/or 400 b. In various embodiments, softwareassociated with controller 332 may include computer program instructionsrelated to, but not limited to, communication protocols, securitysettings, sampling system 304 interaction, cooling system 302sub-controller/controllers 309 a and 309 b, and temperature and pressuresensors in systems 100 a and/or 100 b.

Software may further include computer program instructions pertaining todetermination in a spectroscopy based sub-sampling systems (e.g.,sub-sampling systems 330, 300 a, 300 b, etc.) regarding methods totrigger an excitation system (e.g., see FIGS. 24 to 29) and methods toread outputs from a detection system connected to the source, asdescribed in greater detail below. Exemplary embodiments of softwareassociated with controllers 309 a, 309 b, and 332 are described ingreater detail below with reference to flowcharts of FIGS. 31A, 31B, and32. In some embodiments, software may also cause controllers 309 a, 309b, and 332 (e.g., see FIGS. 19 to 23) to monitor systems 100 a and 100 bfor fluidic leaks and other potential problems.

In an embodiment, a sampling system 304 may periodically poll analyticalsystems 400 a and/or 400 b (e.g., see FIGS. 17 and 18) (e.g., every fewmilliseconds, seconds, minutes, hours, days, etc.) to send or requestspecific commands or instructions. When a sampling system 304 isdeployed (e.g., onboard a ship), installed software may be configuredwith a custom login/password that may be entered by a user to initiatestartup of the system. Upon startup, receipt of the login/password mayinitiate retrieval of configuration settings for sampling system 304.The retrieved settings may contain settings entered into the samplingsystem 304 and any other information that the onboard system 304 maydetect from its own hardware. An example setting may include a samplingschedule and a fluid retention period characterizing a fluid to besampled.

Some deployment situations (e.g., on a ship) provide limited space fordata storage devices, such as an external device associated withanalytical system 400 a. As such, an amount of data storage space may belimited for such an application. Therefore, an onboard system, 100 a or100 b, having sampling system 304, that is deployed in a remotelocation, without network access, may need to drop sampled data once itslimited data storage capacity is exceeded. However, once network 305access becomes available (e.g., see FIGS. 17 and 18), systems 100 aand/or 100 b may establish a connection, via network 305, withanalytical system 400 b that may be implemented in a cloud basedcomputing platform. System 304 may then upload automated sample data toanalytical system 400 b that was previously stored when sampling system304 was disconnected from network 305.

FIG. 24 is a schematic of a sub-sampling system 330 that may be used inthe sampling system of FIG. 23, according to an example embodiment ofthe present disclosure. As mentioned above, use of reference character330 to describe the sub-sampling system of FIG. 24 is for simplicity ofillustration and description and does not imply any particular orderingof sub-sampling systems of sampling system 304 of FIG. 23.

Sub-sampling system 330 may be a removable and replaceablecomponent/system that may be “plugged” into sampling system 304 (e.g.,see FIG. 23) as necessary to perform specific analyses on a sample offluid being routed through sampling system 304. Sub-sampling system 330may generate real-time spectroscopic data regarding the fluid sample.Combining multiple sub-sampling systems 330, 330 a, 330 b, etc., may beaccomplished by simply “plugging” multiple sub-sampling systems 330, 330a, 330 b, etc., together during assembly of the sampling system 304, asshown in FIG. 23. The presence of multiple sub-sampling systems 330, 330a, 330 b, etc., may allow many different types of fluid samples to beanalyzed, and may allow determination of a variety of differentcharacteristics of such samples.

In exemplary embodiments, accurate analysis may be performed and precisedata may be obtained from fluid samples by performing electro-opticalanalysis on sample fluids. Sub-sampling system 330 (e.g., see FIG. 24)may utilize a spectral scanner/spectrometer/custom electro-opticalsystem to instantaneously and continuously scan and record data relatedto the molecular makeup and condition of any fluids such as, forexample, industrial oil and water. Different types of fluids/materialshave unique spectroscopic signatures and an electro-optical system mayread and determine differences between various fluids. In exemplaryembodiments, sub-sampling system 330 may be at least one of a Ramansub-sampling system 330 a (e.g., see FIGS. 25A and 25B), a fluorescencesub-sampling system 330 b (e.g., see FIGS. 26A and 26B), an absorbancesub-sampling system 330 c (e.g., see FIGS. 27A and 27B), a FourierTransform Infra-Red IR absorbance sub-sampling system 330 d (e.g., seeFIGS. 28A and 28B), and an absorbance-fluorescence-scatter sub-samplingsystem 330 e (e.g., see FIG. 29). Each type of electro-optical analysisbased sub-sampling systems 330, 330 a, 330 b, etc., may providecomplementary methods of analyzing fluids by identifying variouscomponents of the fluids.

In exemplary embodiments, sub-sampling system 330 (e.g., see FIG. 24)may include connections between pluggable fluid input and outputconnectors 306 e and 306 f. For example, connector 306 e may be a femaleinput connector on top of sub-sampling system 330. Similarly, connector306 f may be a male output connector on the bottom of sub-samplingsystem 330. Connector 306 e may be configured to be plugged/snappedtogether with a complementary suitable connector of another component ofsampling system 304. In the example of FIG. 23, connector 306 e mayplug/snap into fluidic connection with viscometer 328. Similarly,connector 306 f may be configured to be plugged/snapped together with acomplementary suitable connector of another component of sampling system304, as described in greater detail below.

In the example of FIG. 23, connector 306 f of sub-sampling system 330(e.g., see FIG. 24) couples with connector 306 g of sub-sampling system330 a (e.g., see FIG. 25A). Connector 306 g of sub-sampling system 330a, shown in FIG. 25A, may be a female input connector configured to becoupled with corresponding male output connector 306 f of sub-samplingsystem 330. Similarly, output connector 306 h of sub-sampling system 330a, shown in FIG. 25A, may be a male output connector that is configuredto connect with a complementary connector of sub-sampling system 330 bof FIG. 26A, etc.

Connector 306 e of sub-sampling system 330, shown in FIG. 24, may beconfigured to provide a fluidic connection to a continuous-flow orflow-through electro-optical sampling chamber 340. Connector 306 eallows fluid to enter connector 306 e along path B and to exit connector306 e along path B1. From there, fluid enters electro-optic samplingchamber 340 along path B1 and exits electro-optic sampling chamber 340along path B2. Fluid then flows into connector 306 f along path B2 andexits connector 306 f along path B3. As described in greater detailbelow, with reference to FIG. 25A, fluid enters sub-sampling system 330a through connector 306 g along path B3.

As shown in FIG. 24, sub-sampling system 330 may further include a fiberoptic probe 342. Fiber optic probe 342 may be connected to excitationsource 344 via fiber optic cable 348 a. Fiber optic probe 342 mayfurther be connected to detection system 346 via fiber optic cable 348b. Fiber optic probe 342 may receive radiation from excitation source344 through fiber optic cable 348 a and may provide the receivedradiation to sampling chamber 340 via fiber optic cable 348 c. Fiberoptic probe 342 may further receive reflected/scattered radiation fromsampling chamber 340 via fiber optic cable 348 c. Fiber optic probe 342may then transmit the reflected/scattered radiation, received fromsampling chamber 340, to detection system 346 through fiber optic cable348 b.

In an embodiment, sampling chamber 340, illustrated in FIG. 24, may be aglass, quartz, borosilicate, or polystyrene chamber. Sub-sampling system330 may also include wiring connections W to controller 332 (e.g., seeFIG. 23). As was the case with embodiments illustrated in FIGS. 20 and22, wiring connections W may provide an electrical power connection thatprovides electrical power to components (e.g., excitation source 344 anddetection system 346) of sub-sampling system 330. If, however, if wiringconnections W does not provide an electrical power connection tosubsystem 330, a separate power connection P may be provided.

Wiring connections W, may provide an electrical connection to controller332 (e.g., see FIG. 23). In some embodiments, wiring connections W mayuse a dovetail wiring configuration to inter-connect various componentsof fluid analysis systems 100 a and 100 b (e.g., see FIGS. 17 and 18).In an exemplary embodiment, power plug/connection, P, may be connectedto a power distribution unit PDU (not shown) inside enclosure 300 a orenclosure 300 b of fluid analysis systems 100 a and/or 100 b,respectively. In other embodiments power plug/connection P may beconnected to a PDU of sampling system 304 (not shown).

As shown in FIG. 23, for example, fluid may be routed in to sub-samplingsystem 330 from valve 312 a and/or may be routed to viscometer 328. Fromthere, fluid may be routed into sub-sampling system 330 and then intosampling chamber 340 of sub-sampling system 330, as shown in FIG. 24.Once the fluid has been routed into sampling chamber 340 of sub-samplingsystem 330 an electro-optic analysis of the fluid may be performed.

Controller 332 (e.g., see FIG. 23) may flush a sample of the fluidthrough the chamber 340 of sub-sampling system 330 (e.g., see FIG. 24)for a certain predetermined period of time. The predetermined period oftime may be chosen depending on a fluidic path distance between samplingsystem 304 (e.g., see FIG. 23) and fluid source 200 (e.g., see FIGS. 17and 18). Flushing the sample removes previous fluid from other sources200, 200 a, and/or 200 b (e.g., see FIGS. 17 to 22) and ensures that aclean sample is provided to sampling chamber 340 (e.g., see FIG. 24).Controller 332 may then close relevant input and output valves (e.g.,valves 308 a and 308 b of FIG. 23) in sampling system 304, and/or inputand output valves (e.g., valves 314 a, 314 b, etc., of FIGS. 19 to 22)in respective cooling systems 302 a, 302 b, 302 c, and 302 d to stopfluid flow thereby rendering a still/motionless sample of fluid insampling chamber 340 of FIG. 24.

Controller 332 (e.g., see FIG. 23) may then be used in conjunction withprobe 342, excitation source 344, and detection system 346 (e.g., seeFIG. 24) to obtain real-time spectroscopic data regarding composition ofthe fluid in sampling chamber 340 of FIG. 24. For example, controller332 may begin collecting samples by triggering excitation source 344while simultaneously reading resulting real-time data generated bydetection system 346. The still/motionless nature of the fluid sample inthe sampling chamber 340, after closing input and output valves, mayfurther allow application of time resolved optical spectroscopy to thefluid. Once adequate sampling has been performed on fluid samples andcorresponding relevant real-time data has been obtained, fluid may berouted as shown via arrow B3 in FIG. 24 to another sub-sampling system330 a, 330 b, etc., and/or fluid may be returned to cooling system 304(e.g., see FIG. 23).

In exemplary embodiments, controller 332 (e.g., see FIG. 23) may also beconfigured, based on learned feedback from sampling system 304 of FIG.23, to adjust a focus of probe 342 (e.g., see FIG. 24) by increasing ordecreasing a distance between probe 342 and the sampling chamber 340.While adjusting this distance, controller 332 may continually takeoptical data samples in an attempt to match a known good focus. Theknown good focus may be established via predetermined samples taken froma fluid similar to the specific fluid in question, where the samples mayhave been previously stored in databases 402 a and/or 402 b prior toinstallation of systems 100 a and/or system 100 b, respectively (e.g.,see FIGS. 17 and 18).

A focus calibration process may be issued manually or automaticallyduring a focus run, or the focus calibration process may be based on abaseline standardization sample. In various embodiments, the focaldistance of probe 342 may be adjustable during setup (e.g., via commandsfrom controller 332 of FIG. 23) to maximize resolution of optical datasamples of the fluid. In an embodiment, controller 332 may automaticallyadjust the focus of probe 342, using various mechanical positioningdevices. For example, a worm gear or other adjuster/glide system may beused to adjust focus of probe 342.

Excitation source 344 and detection system 346 (e.g., see FIG. 24) maybe used in tandem to perform fluid analysis. Detection system 346 mayact as electro-optical detector for a given excitation source 344.Controller 332 (e.g., see FIG. 23) may inform detection system 346 toprepare for sampling, after which controller 332 may inform theexcitation source 344 to generate and deliver electro-magnetic radiationinto the fluid sample. Detection system 346 may then register theresults of radiation received from the sampling chamber 340 that waslaunched by the excitation source 344. Excitation source 344 anddetection system 346 may receive control signals (e.g., from controller332 of FIG. 7) from wiring connections W including connections 349 a and349 b, respectively. Similarly, excitation source 344 and detectionsystem 346 may receive electrical power through power connection P fromconnections 349 c and 349 d, respectively.

In exemplary embodiments, the generation of radiation from excitationsource 344 (e.g., see FIG. 24) may occur in milliseconds to secondsdepending on the excitation source used and the type of detectionrequired. In an embodiment, excitation source 344 may be a lightemitting diode LED source (e.g., LED may be a specific chromatic source,a mono-chromatic source, an ultra-violet (UV) source, etc.), aninfrared/near-infrared IR/NIR source, and/or a wavelength stabilizedlaser (i.e., a specific wavelength laser for excitation). In variousembodiments, detection system 346 may be a type of charge-coupled deviceCCD that may simply report direct data without a spectrometer forfiltering, a set of photodiodes with a matching set of spectral filterstuned to respond to specific wavelengths, and/or a spectrometer coupledto a thermally controlled CCD that may detect multiple sources coupledto the spectrometer for filtering.

The excitation source 344 may generate radiation having, wavelengths ina range from 250 nm to 1500 nm. In another embodiment, theelectromagnetic radiation may have first wavelengths of about 680 nm,second wavelengths of about 785 nm, and third wavelengths of about 1064nm. In embodiments including multiple excitation sources, a firstexcitation source apparatus may include a first laser apparatus and asecond excitation source apparatus may include a second laser apparatus.The first laser apparatus may be configured to transmit firstelectromagnetic radiation having wavelengths of about 680 nm, and thesecond laser apparatus may be configured to transmit electromagneticradiation having wavelengths of about 785 nm. In other embodiments, themultiple excitation sources may include a third laser apparatusconfigured to transmit radiation having wavelengths of about 1064 nm.

An excitation source suitable for Raman spectroscopy may provideelectromagnetic radiation in the UV range, for example, 244 nm, 257 nm,325 nm, 364 nm; visible range, for example, 457 nm, 473 nm, 488 nm, 514nm, 532 nm, 633 nm, 660 nm; and NIR range, for example, 785 nm, 830 nm,980 nm, 1064 nm. In further embodiments, an excitation source mayprovide electromagnetic radiation a wavelength of 785 nm.

In some embodiments, sub-sampling systems (e.g., sub-sampling systems330, 330 a, 330 b, etc. of FIG. 23) may be configured to divertapproximately 1 to 10 ml of the fluid samples being analyzed into aretrieval storage compartment/container (not shown) within samplingsystem 304 of FIG. 23. Doing so may allow the fluid sample to beanalyzed via Gas Chromatography/Mass Spectrometry if analytical systems400 a or 400 b determine that properties of a given sample cannot beaccurately identified. In various embodiments, sub-sampling systems 330,330 a, 330 b, etc., may include respective ports allowing removal of thecompartment/container containing a fluid sample that may be shipped toan external location for further processing and analysis.

FIG. 25A is a schematic of a Raman sub-sampling system 330 a that may beused with the sampling system of FIG. 23, according to an exampleembodiment of the present disclosure. As mentioned above, use ofreference character (330 a) to describe the Raman sub-sampling system ofFIG. 25A is for simplicity of illustration and description and does notimply any particular ordering of sub-sampling systems of sampling system304 of FIG. 23.

FIG. 25B is a partial cross-sectional view of a Raman probe 342 a thatmay be used in the Raman sub-sampling system of FIG. 25A, according toan example embodiment of the present disclosure. Raman sub-samplingsystem 330 a includes features that are similar to those of sub-samplingsystem 330 described above with reference to FIG. 24. For example, Ramansub-sampling system 330 a includes connector 306 g that receives fluidalong path B3 from sub-sampling system 330 described above withreference to FIGS. 23 and 24.

Fluid enters Raman sub-sampling system 330 a along path B3 throughconnector 306 g and exits connector 306 g along path B4. Fluid thenenters sampling chamber 340 a along path B4 and exits sampling chamber340 a along path B5. Fluid then enters connector 306 h and exits Ramansub-sampling system 330 a through connector 306 h along path B6. Asdescribed in greater detail below, with reference to FIG. 26A, fluid maythen enter sub-sampling system 330 b along path B6 though connector 306i. Other features of Raman sub-sampling system 330 a that are similar tosub-sampling system 330 of FIG. 24 include fiber optic cables 448 a and448 b, wiring connections W, and power connection P.

Differences between sub-sampling system 330 of FIG. 24, and Ramansub-sampling system 330 a of FIG. 25, relate to Raman probe 342 a,excitation source 344 a, and detection system 346 a. Excitation source344 a and detection system 346 a may receive control signals (e.g., fromcontroller 332 of FIG. 7) from wiring connections W includingconnections 349 a and 349 b, respectively. Similarly, excitation source344 a and detection system 346 a may receive electrical power throughpower connection P from connections 349 c and 349 d, respectively.

In exemplary embodiments, spectral data obtained from a fluid sample maybe obtained from a Raman sub-sampling system 330 a that uses anexcitation source having a single frequency, and that uses a specializedRaman probe 342 a to capture the frequency shifted light (i.e., Ramanscattered light) that characterizes molecular vibrational energy levels.In exemplary embodiments, Raman sub-sampling system 330 a may include aspecialized Raman probe 342 a, a stabilized wavelength laser 344 a, anda set of photo diodes and spectral filters as part of detection system346 a that targets frequencies characterizing various Raman shifts.

In various embodiments, sampling chamber 340 a, illustrated in FIG. 25A,may be similar to sampling chamber 340 described above with reference toFIG. 24. In this regard, sampling chamber 340 a may be a quartz or glassflow-through/continuous flow chamber based on a wavelength and power ofthe laser 344 a. For example, if the laser 344 a is in the UV range,then chamber 340 a may be a quartz chamber. In exemplary embodiments,laser 344 a may be a 785 nm wavelength laser. In various embodiments,Raman probe 342 a may be a General Purpose Raman Probe offered by OceanOptics, Inc.

As shown in FIG. 25B, the excitation electro-magnetic source 344 a shownin FIG. 25A may generate radiation that may be emitted into excitationfiber 348 a which then enters Raman probe 342 a, as shown in FIG. 25B.Inside Raman probe 342 a, the radiation may exit excitation fiber opticcable 348 a and pass through lens 350 a that acts to defocus theradiation. The radiation may then pass through a band-pass frequencyfilter 352 a and a dichroic filter 352 b of the Raman probe 342 a. Lowpass frequency filter 352 a, dichroic filter 352 b, and low-passfrequency filter 352 c (described below) are used to provide selectedfrequencies/wavelengths to the sampling chamber 340 a and to receiveselected frequencies/wavelengths from the sampling chamber 340 a. Forexample, in a Raman spectroscopy measurement, incident radiation havinga first selected band of frequencies may be incident on the samplingchamber 340 a. A Raman signal includes frequencies that have beenshifted (i.e., Raman shifted frequencies) from the frequency of theincident radiation. The use of filters 352 a, 352 b, and 352 c, allowsproper selection of incident and received frequencies.

Radiation that passes though dichroic filter 352 b may propagate throughlens 350 b, which acts to focus the radiation. The focused radiation mayexit Raman probe 342 a and enter sampling chamber 340 a (e.g., see FIGS.25A and 25B). Upon entering sampling chamber 340 a, the radiation may befocused to a small region 354 of fluid in sampling chamber 340 a (e.g.,see FIG. 25B). Some of the radiation incident on the small region 354 offluid may be scattered/reflected back out of the sampling chamber 340 aand may re-enter Raman probe 342 a through lens 350 b which acts todefocus the scattered/reflected radiation. Some of the defocusedscattered/reflected radiation may be reflected from dichroic filter 352b and may be directed to mirror 356 (e.g., see FIG. 25B). Radiationhitting mirror 356 may then be reflected from mirror 356 and maypropagate through low-pass frequency filter 352 c. From there, radiationpassing through low-pass frequency filter 352 c may be incident on lens350 c which acts to focus the radiation and direct the focused radiationinto collection fiber optic cable 348 b.

The radiation may then be transported via collection fiber optic cable348 b and may be collected on photodiodes of detection system 346 a ofFIG. 25A. Raman probe 342 a may be used to measure the frequency orequivalently, wavelength shifts (i.e., the Raman shifts) of the excitedsample. These Raman shifts may show up as peaks in a spectral graph. TheRaman shifts expressed in wavenumber units may be converted to otherunits (e.g., wavelength, frequency, electron volt eV) via the followingformulae:

$\begin{matrix}{{Wavenumbers} - {Wavelength}} & {\overset{˘}{v} - \frac{10000}{\lambda}} \\{{Wavenumbers} - {Frequency}} & {\overset{˘}{v} - \frac{v}{100 \cdot c}} \\{{Wavenumbers} - {{Electron}\mspace{14mu} {volt}}} & {\overset{˘}{v} - {\frac{e}{h \cdot c} \cdot \frac{E}{100}}}\end{matrix}$

v̆: Wavenumbers cm⁻¹

λ: Wavelength μm

ν: Frequency s⁻¹c: Velocity of light 2.99792458·10⁸ m/se: Elementary charge 1.60217733·10⁻¹⁹ Ch: Planck's constant 6.6260755·10⁻³⁴ J·s

E: Energy eV

In exemplary embodiments, spectral data of the fluid sample may beobtained by measuring/determining the values of various Raman shifts.

FIG. 26A is a schematic of a fluorescence sub-sampling system 330 b thatmay be used with the sampling system of FIG. 23, according to an exampleembodiment of the present disclosure. As mentioned above, use ofreference character (330 b) to describe the fluorescence sub-samplingsystem of FIG. 26A is for simplicity of illustration and description anddoes not imply any particular ordering of sub-sampling systems ofsampling system 304 of FIG. 23.

FIG. 26B shows a reflection probe 342 b that may be used in thefluorescence sub-sampling system of FIG. 26A, according to an exampleembodiment of the present disclosure. Fluorescence sub-sampling system330 b may include features that are similar to features of sub-samplingsystems 330 and 330 a described above with reference to FIGS. 24 and25A, respectively. For example, fluorescence sub-sampling system 330 bincludes connector 306 i that receives fluid along path B6 fromsub-sampling system 330 a described above with reference to FIGS. 23 and25A. Fluid enters fluorescence sub-sampling system 330 b along path B6through connector 306 i and exits connector 306 i along path B7. Fluidthen enters sampling chamber 340 b along path B7 and exits samplingchamber 340 b along path B8. Fluid then enters connector 306 j alongpath B8 and exits fluorescence sub-sampling system 330 b throughconnector 306 j along path B9. As described in greater detail below,with reference to FIG. 27A, fluid may then enter sub-sampling system 330c along path B9 though connector 306 k. Other features of fluorescencesub-sampling system 330 b that are similar to sub-sampling system 330 ofFIG. 24, and sub-sampling system 330 a of FIG. 25A, include fiber opticcables 348 a and 348 b, wiring connections W, and power connection P.

Differences between sub-sampling system 330 of FIG. 24, Ramansub-sampling system 330 a of FIG. 25A, and fluorescence sub-samplingsystem 330 b of FIGS. 26A and 26B, relate to fluorescence probe 342 b,excitation source 344 b, and detection system 346 b, as described ingreater detail below. Excitation source 344 b and detection system 346 bmay receive control signals (e.g., from controller 332 of FIG. 7) fromwiring connections W including connections 349 a and 349 b,respectively. Similarly, excitation source 344 b and detection system346 b may receive electrical power through power connection P fromconnections 349 c and 349 d, respectively.

Fluorescence spectroscopy based systems utilize electro-magneticspectroscopy to analyze fluorescence from a sample. These systems mayinvolve using a beam of light, usually UV light, that excites electronsin atoms/molecules of certain compounds and, in turn, causes them toemit light; typically, but not necessarily, visible light. Fluorescencesub-sampling/detection systems (e.g., such as fluorescence sub-samplingsystem 330 b of FIG. 26A) generally include: an excitation light source344 b, a fluorophore fluorescent chemical compound that can re-emitlight upon light excitation, wavelength filters to isolate emissionphotons from excitation photons, and a detection system 346 b thatregisters emission photons and produces a recordable output, usually asan electrical signal.

Spectral data of a fluid sample may be obtained from a fluorescencesub-sampling system 330 b based on the following technology. Use of alight source 344 b (e.g., see FIG. 26A) that emits broadband light(i.e., light including many frequencies) provides photons in variousenergies. When the light source 344 b emits radiation that is directedto be incident on a fluid/oil sample, incident photons penetrate intothe sample and interact with atoms/molecules of the sample. Photonshaving energies equal to a difference in atomic or molecular energylevels may be absorbed. Incident photons that are not absorbed may bescattered. Photons may also be emitted by excited atoms and molecules.Light leaving the sample 340 b, therefore, includes scattered andemitted photons that may be detected. Absorbed light in a fluorescencespectrum is seen as attenuated or missing bands of frequencies. Theresulting fluorescence spectrum, which is material dependent, may becompared to the incident radiation to provide the desired spectralinformation that may be used to identify one material relative toanother.

In exemplary embodiments, fluorescence sub-sampling system 330 b mayinclude a reflection probe 342 b, and an electromagnetic radiationsource, e.g., LED, UV, or laser source 344 b connected to the probe 342b via fiber optic cables 348 a, and a detection system 346 b connectedto probe 342 b via fiber optic cables 348 b. Sampling chamber 340 b maybe similar to sampling chamber 340 of FIG. 24, and similar to samplingchamber 340 a of FIG. 25A. In this regard, chamber 340 b may beconfigured using any acceptable materials conducive to the collection ofoptical and/or spectral data. Examples of materials which may formchamber 340 b include: quartz or polystyrene flow throughcell/continuous flow chamber. For example, if source 344 b is a lowpower LED source, then chamber 340 b may be a polystyrene chamber. Inexemplary embodiments, source 344 b may be a 240-627 nm LED source.Alternatively, a UV source 344 b may be utilized if a wider sourcespectral range is needed. In an embodiment, reflection probe 342 b maybe a premium-grade reflection probe manufactured by Ocean Optics, Inc.,as illustrated, for example, in FIG. 26A.

In various embodiments, detector 346 b may be a fluorometer that mayrequire a spectral filter to block radiation having frequencies equal tothose of the excitation source. A spectral filter may also be used todetect other wavelengths from source 344 b. In embodiments, detectionsystem 346 b may utilize a set of photodiodes with spectral filters.Detection system 346 b may also include a CCD device. In eitherembodiment, a detected fluorescence signal may be converted to anelectrical signal by detection system 346 b. The converted electricalsignal may then be transmitted to the controller 332 (e.g., see FIG.23), which may determine useful spectral information of the fluidsample.

FIG. 27A is a schematic of an absorbance sub-sampling system 330 c thatmay be used with the sampling system of FIG. 23, according to an exampleembodiment of the present disclosure. As mentioned above, use ofreference character 330 c to describe the absorbance sub-sampling systemof FIG. 27A is for simplicity of illustration and description and doesnot imply any particular ordering of sub-sampling systems of samplingsystem 304 of FIG. 23.

FIG. 27B shows a transmission dip probe 342 c that may be used in theabsorbance sub-sampling system 330 c of FIG. 27A, according to anexample embodiment of the present disclosure.

Absorbance sub-sampling system 330 c may include features that aresimilar to features of sub-sampling systems 330, 330 a, and 330 b,described above with reference to FIGS. 24, 25, and 26, respectively.For example, absorbance sub-sampling system 330 c includes connector 306k that receives fluid along path B9 from sub-sampling system 330 bdescribed above with reference to FIGS. 23 and 26A.

Fluid enters absorbance sub-sampling system 330 c along path B9 throughconnector 306 k and exits connector 306 k along path B10. Fluid thenenters sampling chamber 340 c along path B10 and exits sampling chamber340 c along path B11. Fluid then enters connector 306 e along path B11and exits absorbance sub-sampling system 330 c through connector 306 ealong path B12. As described in greater detail below, with reference toFIG. 28A, fluid may then enter sub-sampling system 330 d along path B12though connector 306 m. Other features of absorbance sub-sampling system330 c that are similar to sub-sampling system 330 of FIG. 24,sub-sampling system 330 a of FIG. 25, and sub-sampling system 330 b ofFIG. 26A, include fiber optic cables 348 a and 348 b, wiring connectionsW, and power connection P.

Differences between sub-sampling system 330 of FIG. 24, Ramansub-sampling system 330 a of FIG. 25A, fluorescence sub-sampling system330 b of FIG. 26A, and absorbance sub-sampling system 330 c of FIGS. 27Aand 27B, relate to transmission dip probe 342 c, excitation source 344c, and detection system 346 c, as described in greater detail below.Excitation source 344 c and detection system 346 c may receive controlsignals (e.g., from controller 332 of FIG. 23) from wiring connections Wincluding connections 349 a and 349 b, respectively. Similarly,excitation source 344 c and detection system 346 c may receiveelectrical power through power connection P from connections 349 c and349 d, respectively.

Absorbance spectroscopy, commonly referred to as spectrophotometry, isthe analytical technique based on measuring an amount of light absorbedby a sample at a given frequency or equivalently, at a given wavelength.Molecular absorption spectroscopy in the UV and visible portions of theelectro-magnetic spectrum characterizes measured absorption of radiationin its passage through a gas, a liquid, or a solid. Generally, thewavelength region used may be from approximately 190 nm to 1000 nm, andthe absorbing medium may be at room temperature.

In disclosed embodiments, obtaining spectral data of a sample viaabsorbance spectroscopy may include methods similar to those describedabove, with reference to FIG. 26A, for obtaining spectral datacharacterizing a sample via fluorescence spectroscopy. In exemplaryembodiments, a broadband light source 344 c may emit radiation that maybe directed to be incident on a sample 340 c. Radiation emerging fromthe sample 340 c may then be collected and the frequency content of thelight may be analyzed by detection system 346 c. The molecularcomposition of the sample may then be analyzed and determined bycomparing the frequency of incident light that with frequencies oftransmitted light.

In exemplary embodiments, absorbance sub-sampling system 330 c mayinclude a transmission dip probe 342 c, a NIR source 344 c connected toprobe 342 c via fiber optic cables 348 a, and a detection system 346 cconnected to transmission dip probe 342 c via fiber optic cables 348 b.Detection system 346 c measures output transmission of electro-magneticradiation originating from source 344 c, after passing through thesampling chamber 340 c, and returning to detection system 346 c. Acalculated difference in intensity of input and output electro-magneticradiation as a function of frequency or equivalently vs. wavelength isthe absorbance spectrum. Such an absorbance spectrum provides a usefulcharacterization of a material sample.

Sampling chamber 340 c of FIG. 27A may be similar to sampling chamber340 of FIG. 24, sampling chamber 340 a of FIG. 25A, and sampling chamber340 b of FIG. 26A. In this regard, chamber 340 c may be a quartzflow-through cell/continuous flow chamber.

In exemplary embodiments, source 344 c may include a NIR source emittingradiation having wavelengths from 1000 nm to 5000 nm. Source 344 c maybe connected to transmission dip probe 342 c via fiber optic cables 348a. Alternatively, as described above, a UV source 344 c may be utilizedif a wider source range is needed. In some embodiments, source 344 c maygenerate radiation having IR and/or visible wavelengths (e.g., in awavelength range from 100 nm to 10,000 nm). In an embodiment,transmission dip probe 342 c may be a TP300-Series Transmission Probeoffered by Ocean Optics, Inc., as illustrated, for example, in FIG. 27B.In various embodiments, detection system 346 c may utilize a CCD or aset of photodiodes with spectral filters. The spectral filters may beconfigured to measure intensity of resultant transmittedelectro-magnetic energy vs. wavelength or equivalently vs. frequency incomparison to a spectrum of electro-magnetic radiation emitted by source344 c.

FIG. 28A is a schematic of a Fourier Transform Infra-Red (FTIR)absorbance sub-sampling system 330 d that may be used with the samplingsystem of FIG. 23, according to an example embodiment of the presentdisclosure. As mentioned above, use of reference character 330 d todescribe the FTIR absorbance sub-sampling system of FIG. 28B is forsimplicity of illustration and description and does not imply anyparticular ordering of sub-sampling systems of sampling system 304 ofFIG. 23.

FIG. 28B is an illustration of an FTIR process performed by the FTIRabsorbance sub-sampling system 330 d of FIG. 28A, according to anexample embodiment of the present disclosure.

FTIR absorbance sub-sampling system 330 d may include features that aresimilar to features of sub-sampling systems 330, 330 a, 330 b, and 330c, described above with reference to FIGS. 24, 25, 26, and 27,respectively.

For example, FTIR absorbance sub-sampling system 330 d includesconnector 306 m that receives fluid along path B12 from sub-samplingsystem 330 c described above with reference to FIGS. 27A and 27B. Fluidenters FTIR absorbance sub-sampling system 330 d along path B12 throughconnector 306 m and exits connector 306 m along path B13. Fluid thenenters sampling chamber 340 d along path B13 and exits sampling chamber340 d along path B14. Fluid then enters connector 306 n along path B14and exits absorbance sub-sampling system 330 d through connector 306 nalong path B15. As described in greater detail below, with reference toFIG. 29, fluid may then enter sub-sampling system 330 e along path B15though connector 306 o. Other features of absorbance sub-sampling system330 d that are similar to sub-sampling system 330 of FIG. 24,sub-sampling system 330 a of FIG. 25A, sub-sampling system 330 b of FIG.26A, and sub-sampling system 330 c of FIG. 27A, include fiber opticcables 348 a, 348 b, and 348 c, wiring connections W, and powerconnection P.

Differences between sub-sampling system 330 of FIG. 24, Ramansub-sampling system 330 a of FIG. 25A, fluorescence sub-sampling system330 b of FIG. 26A, absorbance sub-sampling system 330 c of FIG. 27, andFTIR absorbance sub-sampling system 330 d of FIG. 28A, relate to probe342 d, excitation source 344 d, interferometer 344 d 1, and detectionsystem 346 d. Excitation source 344 d and detection system 346 d mayreceive control signals (e.g., from controller 332 of FIG. 23) fromwiring connections W including connections 349 a and 349 b,respectively. Similarly, excitation source 344 d and detection system346 d may receive electrical power through power connection P fromconnections 349 c and 349 d, respectively.

FTIR is a form of absorbance spectroscopy used to obtain an infraredspectrum of absorption or emission of a solid, liquid or gas. An FTIRspectrometer may simultaneously collect high spectral resolution dataover a wide spectral range. In exemplary embodiments, obtaining spectraldata of a sample via FTIR may include general methods similar to thoseused for obtaining spectral data of a sample via absorbancespectroscopy, as described above with reference to FIGS. 27A and 27B.For example, infrared IR radiation may be first passed through thesample. Some of the IR radiation may be absorbed by the sample and someof it may pass through (i.e., may be transmitted). The resultingspectrum characterizes the molecular absorption and transmission,thereby creating a specific spectral pattern representative of thesample. The spectral data includes absorption peaks which correspond tothe frequencies of vibrations between the bonds of the atoms making upthe sample. Because each different material constitutes a uniquecombination of atoms, no two compounds produce the exact same IRspectrum, thereby allowing for positive identification of differentkinds of material via qualitative/quantitative analysis. In fact, thesize of the absorption peaks in the spectrum may be used to determinerelative amounts of each material present in the sample.

In exemplary embodiments, FTIR absorbance sub-sampling system 330 d mayinclude features that are similar to features of absorbance sub-samplingsystem 330 c, described above, including a transmission dip probe 342 d,a NIR source 344 d, and detector 346 d. FTIR absorbance sub-samplingsystem 330 d may further include an interferometer 344 d 1 betweensource 344 d and probe 342 d to measure response to an entire range ofwavelengths of a sample at once, as illustrated, for example, in FIGS.28A and 28B.

In various embodiments, chamber 340 d may be a quartz flow-throughcell/continuous flow chamber. In exemplary embodiments, source 344 d maybe a 1000 nm to 5000 nm NIR source connected to transmission dip probe342 d via fiber optic cable 348 a.

Inset 358 a of FIG. 28B is an illustration of a process of performing ameasurement using FTIR absorbance sub-sampling system 330 d. In a firststage 1, an IR beam may be emitted from the source 344 d towardsinterferometer 344 d 1. In stage 2, the IR beam may then enterinterferometer 344 d 1 which transforms the IR beam to generate aninterferogram signal 358 b. The interferogram signal 358 b may then exitthe interferometer and may propagate toward sampling chamber 340 d. Instage 3, the interferogram signal 358 b may be transmitted through orreflected off of the surface of the fluid sample in chamber 340 d,depending on the type of analysis being accomplished. Components of theincident beam are absorbed at specific frequencies. The specificabsorbed components characterize the material in sampling chamber 340 d.In stage 4, output signal 358 c from the sample is passed to detectionsystem 346 d for measurement.

Detection system 346 d may be specially designed to measure the specialinterferogram signal 358 c. The measured signal may then be digitizedand sent to controller 332 in sampling system 304 (e.g., see FIG. 23),which may send the signal to analytical system 400 a or 400 b.Analytical system 400 a or 400 b may perform a Fourier Transform on thedetected signal to generate the final IR spectrum. Comparing the FTIRspectrum to a background spectrum measurement with no sample in the beammay allow for identification of spectral features generated by thesample. In exemplary embodiments, analytical system 400 a or 400 b(e.g., see FIGS. 17 and 18) may decode the signal received fromcontroller 332 using FTIR calculations to obtain the spectral data of afluid sample, as shown, for example, in inset 358 d of FIG. 28A.

FIG. 29 is a schematic of an absorbance/fluorescence/scattersub-sampling system 330 e that may be used with the sampling system ofFIG. 23, according to an example embodiment of the present disclosure.Absorbance/fluorescence/scatter sub-sampling system 330 e may includefeatures that are similar to features of sub-sampling systems 330, 330a, 330 b, 330 c, and 330 d, described above with reference to FIGS. 24to 28, respectively. For example, absorbance/fluorescence/scattersub-sampling system 330 e includes connector 306 o that receives fluidalong path B15 from sub-sampling system 330 d described above withreference to FIGS. 28A and 28B. Fluid entersabsorbance/fluorescence/scatter sub-sampling system 330 e along path B15through connector 306 o and exits connector 306 o along path B16. Fluidthen enters sampling chamber 340 e along path B16 and exits samplingchamber 340 e along path B17. Fluid then enters connector 306 p alongpath B17 and exits absorbance sub-sampling system 330 e throughconnector 306 p along path B18. Fluid leaving along path B18 may thenflow into other sub-sampling systems of sampling system 304 or may flowout of sampling system 304 along path D, as discussed above andillustrated, for example, in FIG. 23.

Other features of absorbance sub-sampling system 330 e that are similarto sub-sampling system 330 of FIG. 24, sub-sampling system 330 a of FIG.25A, sub-sampling system 330 b of FIG. 26A, sub-sampling system 330 c ofFIG. 27A, and sub-sampling system 330 d of FIG. 28A, include fiber opticcables 348 a, 348 b, and 348 c, wiring connections W, and powerconnection P.

Differences between sub-sampling system 330 of FIG. 24, Ramansub-sampling system 330 a of FIG. 25A, fluorescence sub-sampling system330 b of FIG. 26A, absorbance sub-sampling system 330 c of FIG. 27A, andFTIR absorbance sub-sampling system 330 d, relate to probe 342 e,excitation source 344 e, and detection system 346 e, as well asadditional fiber optic cables 348 d and 348 e, that connect probe 342 eto detection system 346 e. Excitation source 344 e and detection system346 e may receive control signals (e.g., from controller 332 of FIG. 23)from wiring connections W including connections 349 a and 349 b,respectively. Similarly, excitation source 344 e and detection system346 e may receive electrical power through power connection P fromconnections 349 c and 349 d, respectively.

Absorbance/fluorescence/scatter sub-sampling system 330 e may combinefeatures of both the fluorescence 330 b and absorbance 330 csub-sampling systems described above with reference to FIGS. 26A and27A, respectively. In exemplary embodiments,absorbance/fluorescence/scatter sub-sampling system 330 e may include areflection and/or transmission dip probe 342 e, multiple sources 344 econnected to the probes 342 e, and a detection system 346 e connected tothe probes 342 e that may measure the output transmission from thesources 344 e after passing through the sampling chamber 340 e, wherethe difference between the input and output vs. frequency is theabsorption spectrum. The absorption spectrum serves as the spectral dataof the sample.

In various embodiments, chamber 340 e may be a flow-throughcell/continuous flow chamber. As described above, chamber 340 e may bequartz flow-through cell. In other embodiments, other suitable materialsmay be used for chamber 340 e based on their ability to transmitincident electromagnetic radiation. In exemplary embodiments, sources344 e may include multiple sources independently connected to reflectionand/or transmission dip probes 342 e via fiber optic cables 348 a.

In various embodiments, detection system 346 e may utilize a CCD or aset of photodiodes with spectral filters for measuring intensities ofvarious frequency components compared to those of the source 344 e. Inexample embodiments, the use of multiple sources 344 e may requireadditional fiber optic cables, 348 d and 348 e, connected to probe 342 ewith multiple fiber optic receivers for each cable 348 d, 348 e, etc.(i.e., a different set of photo diodes in detection system 346 e fordetection of spectral data from the sample for each type of spectroscopysystem used). Using additional fiber optic cables, 348 d and 348 e, mayallow measurement of different types of spectral information throughapplication of various spectral filters for a given excitation source344.

Although various embodiments described herein refer to analysis of oil,fluid analysis systems 100 a and 100 b, as described above withreference to FIGS. 17 and 18, including cooling system 302, samplingsystem 304, and/or analytical systems 400 a and 400 b, may be used toanalyze properties of other types of fluids, including water. In anexemplary embodiment, fluid analysis systems 100 a and 100 b may be awater analysis system. In embodiments of systems 100 a and 100 b, watermay be routed from a water source 200 (e.g., a reservoir), into coolingsystem 302 and/or directly into sampling system 304 to obtain real-timedata regarding the fluid. For example, water may be analyzed indisclosed embodiments to determine a presence of microorganisms,nitrate, and arsenic.

In various embodiments, cooling system 302, sampling system 304, and/oranalytical systems 400 a and 400 b of water analysis systems 100 a and100 b may include features that are similar to oil analysis systems 100a and 100 b described above with reference to FIGS. 18 to 23. However,in some embodiments, cooling system 302 of water analysis systems 100 aand 100 b may not include a filter 320 (e.g., see FIGS. 19 to 22). As inoil analysis system 100 a and 100 b, cooling system 302 may not beutilized in water analysis systems 100 a and 100 b if water is at asufficiently low temperature for analysis via sampling system 304. Invarious embodiments, sampling system 304 of water analysis systems 100 aand 100 b may or may not include a viscometer 328 (e.g., see FIG. 23).

Although fluid analysis systems 100 a and 100 b are shown in FIGS. 18 to29, and described above as having specificconfigurations/features/applications, these systems are not limited tothese particular configurations/features/applications and otherconfigurations/features/applications may be utilized as suitable toperform analysis of various types of fluids.

FIG. 30A is a schematic of a multi-source fluid sampling system 3000,according to an example embodiment of the present disclosure. System3000 includes a single excitation source 344, a single detection system346, and a plurality of sample chambers 340 f, 340 g, 340 h, and 340 i.In this example, each of the sample chambers 340 f, 340 g, 340 h, and340 i may be directly connected to respective fluid sources (not shown).For example, fluid sources may be internal combustion engines,generators, or other mechanical devices containing fluids of interest.In this example, four sources may be accommodated. Other numbers offluid sources may be accommodated in other example embodiments. Forexample, an embodiment may have 5 fluid sources, 6 fluid sources, 7fluid sources 8, fluid sources, etc.

System 3000 may further include an optical switch 5390 that may beconfigured to route electromagnetic energy, received from excitationsource 344, to the various sample chambers 344 f, 344 g, 344 h, and 344i. Electromagnetic radiation received from optical switch 5390 maythereby interact with fluid in respective sample chambers 344 f, 344 g,344 h, and 344 i. System 3000 may further include passive opticalcoupler 3002. Optical coupler 3002 may be configured to receiveelectromagnetic radiation emitted by fluid in sample chambers 344 f, 344g, 344 h, and 344 i, in response to interaction of the electromagneticradiation received from optical switch 5390. The radiation received byoptical coupler 3002 may be combined and provided to optical detectionsystem 346 via optical fiber cables. System 3000 may further includecontrol hardware 3004 which may include control circuitry and/or one ormore computational devices.

According to an embodiment, excitation source 344 may be a single Ramanlaser and detection system 346 may include a single spectrometer. Infurther embodiments, the excitation system 344 may include two or morelasers that generate electromagnetic radiation at two or more respectivefrequencies. In certain embodiments, optical switch 5390 may beconfigured to direct the Raman laser excitation signal to one sampletest chamber at a time. In another embodiment, the Raman laser signalmay be split or routed to multiple sample test chambers simultaneously.

Optical switch 5390 may be controlled by a combination of controllerhardware 3004 and/or software that may select a specific sample chamberto which the Raman laser excitation signal may be routed. Followingsample excitation by a Raman laser 344 excitation signal, sample Ramanemission data may be collected by a single Raman emission detector 346by using optical coupler 3002. Optical coupler 3002 may merge collectionoptical fibers of respective sample chambers into a single opticalconnection. The single optical connection may be further connected to aRaman spectrometer 346 configured to collect Raman emission data.

In certain embodiments, when the Raman excitation signal is routed toone sample chamber at a time via optical switch 5390, optical coupler3002 may passively sum the Raman emission signal from each sample testchamber. Raman emission signals derived from each sample test chambermay be continuously communicated to Raman spectrometer 346. Use ofpassive optical coupler 3002 may be advantageous in that it generallyexhibits less attenuation of the Raman emission signal compared to useof a second optical switch for routing Raman emission signals to asingle detection system. For example, a passive coupler (e.g., suchoptical coupler 3002) may exhibit only marginal signal attenuation,while an active optical switch (e.g., such as optical switch 5390) mayattenuate the signal by an amount on the order of 15% of the signal,even for high-performance switches.

FIG. 30B is a schematic of a fluid sampling system 3000 a, according toan example embodiment of the present disclosure. System 3000 a includesmultiple excitation sources, an optical switch, power, and controlcircuitry, according to an example embodiment of the present disclosure.System 3000 a may provide an advantage in that all of the electronic andoptical components may be housed in a single enclosure 3006. Asdescribed in greater detail below with reference to FIGS. 30B and 30C,system may be passively or actively cooled. Such cooling may provideimproved performance of excitation sources (e.g., lasers, LEDs, etc.)and detection systems (e.g., CCD devices). As described below, system3000 a may be actively cooled to a temperature of 100° C. below ambienttemperature. In other embodiments, other temperatures may be achievedincluding 5° C. below ambient, 10° C. below ambient, 20° C. belowambient, etc. Such cooling may allow greatly improved signal detection.

System 3000 a, indicated in the top part of FIG. 30B (part A, shownabove the dashed line) contains all of the electrical and opticalsystems that may be housed in a rugged, water tight enclosure 3006. Asdescribed in greater detail below, system 3000 a provides fiber opticconnections to external systems (part B, shown below the dashed line).In this way, fluids associated with external systems (B, below thedashed line) are kept separate from the electrical and opticalcomponents of system 3000 a (A, above the line).

In this example, system 3000 a may include first 3008 a and second 3008b excitation sources. For example, excitation source 3008 a may be alaser that emits electromagnetic radiation at a wavelength of 785 nm.Further excitation source 3008 b may be a laser that emitselectromagnetic radiation at a wavelength of 680 nm. On otherembodiments, various other excitation sources may be provided thatgenerate various wavelengths of electromagnetic radiation (e.g., IR,visible, UV, etc.) Excitation sources 3008 a and 3008 b may both beelectrically connected to, and controlled by, an excitation sourcecontroller 3010. Excitation source controller 3010 may further becoupled to programmable micro-controller 3012. Micro-controller 3012 mayserve as a master controller for system 3000 a and may generate controlsignals for the various sub-systems and may communicate data withexternal systems.

In exemplary embodiments, controller 3012 may be the Raspberry Pi 3Model B, Raspberry Pi Zero, or Raspberry Pi 1 Model A+. In otherembodiments, controller 3012 may be the Mojo Board V3 offered byEmbedded Micro—an FPGA (Field Programmable Gate Array) with multiplepre-made shields. In further embodiments, any other suitable controller3012 may be used.

Electromagnetic radiation generated by excitation sources 3008 a and3008 b may be provided to an optical combiner 3014 (e.g. dichroiccombiner) by respective optical fiber cables 3016 a and 3016 b.Electromagnetic radiation provided to optical combiner 3014 (e.g.dichroic combiner) may be provided to optical switch 3018 via opticalfiber cable 3016 c.

Electromagnetic radiation may be provided to optical output connectors3020 via various fiber optic cables 3016 d, 3016 e, 3016 f, 3016 g, etc.Optical output connectors 3020 may be used to provide opticallyswitchable electromagnetic radiation to a plurality of external samplingchambers (e.g., sample chambers 340 f, 340 g, 340 h, 340 i, etc., ofFIG. 30A).

In this example, optical output connectors 3020 are shown providingelectromagnetic radiation to external systems E1, E2, E3, and E4 throughrespective fiber optic cables 3017 d, 3017 e, 3017 f, and 3017 g.Systems E1, E2, E3, and E4 may be sampling chambers associated withrespective fluid sources.

After interacting with fluid samples in one or more external samplingchambers (e.g., of systems E1, E2, E3, and E4), electromagneticradiation may be received by system 3000 a via optical input connectors3022. Electromagnetic radiation may be received by optical inputconnectors 3022 via various fiber optic cables 3017 h, 3017 i, 3017 j,3017 k, etc. Electromagnetic radiation received by optical inputconnectors 3022 may be provided to optical coupler 3024 via variousfiber optic cables 3016 h, 3016 i, 3016 j, 3016 k, etc. Electromagneticradiation may be combined by combiner 3024 and then provided todetection system 3026 via fiber optic cable 3016 m. Data generated bydetection system 3026 may then be provided to controller 3012. Asdescribed above, other sensors (e.g., sensors S1, S2, S3, and S4) may beprovided to measure other quantities such as viscosity, temperature,particle counts, etc. Information from the various sensors may begathered by a sensor board 3028, which in turn, may provide such sensordata to controller 3012.

System 3000 a may further include a CAN controller 3030 that maycommunicate with controller 3012 via connector 3031 a and maycommunicate with external systems through CAN connections 3032 throughconnection 3031 b. As described above, CAN controller 3030 may receivedata from various external sensors S1, S2, S3, and S4 through respectiveelectrical or optical channels 3044 a, 3044 b, 3044 c, and 3044 d, asdescribed below. For example, sensors S1, S2, S3, S4, may be configuredto generate data from one or more external systems. For example, sensorsS1, S2, S3, and S4 may include temperature and/or viscometers that maymake measurements on respective systems E1, E2, E3, E4.

Data generated by detection system 3026 may also be communicated toexternal systems through CAN connections 3032 through variousconnections 3044 a, 3044 b, 3044 c, and 3044 d. System 3000 a mayfurther include a cellular modem 3034 that may communicate throughwireless channels with external systems by providing signals to one ormore communication devices 3036. In an embodiment, communication device3036 may be an antenna that generates wireless signals. Cellular modem3034 may further communicate with and receive control signals fromcontroller 3012 via electrical or optical connection 3035.

System 3000 a may further include an external power supply connection3038 that may be connected to an AC/DC converter 3040 and one or more DCcurrent/voltage supplies 3042 a and 3042 b. Communication between system3000 a and various other systems may be provided through connections toa wiring harness 3037.

System 3000 a may be cooled with one or more cooling systems. Forexample, system 3000 a may include an air intake vent 3034 a and an airexhaust vent 3034 b. A fan 3036 may further be provided to force airfrom the air intake vent 3034 a to the air exhaust vent 3034 b tothereby remove waste heat from enclosure 3006 generated by the variouscomponents of system 3000 a. Forced air cooling, as provided by vents3034 a and 3034 b and fan 3036 (e.g., see FIG. 30B) only removes heatgenerated by components within system 3000 a. As such, forced aircooling may only be used to keep fluid sampling system 3000 a at anambient temperature. In certain circumstances, it may be advantageous toactively cool detection system 3026 or other components to a temperaturebelow ambient temperature.

System 3000 a may further be configured to include one or more coolingadditional cooling systems 3036 for cooling one or more components ofthe optical path, as described in great detail below with reference toFIG. 30B. The optical path may be defined to include any component thattransmits, routes, receives, and detects electromagnetic radiation aspart of system 3000 a. For example, components of the optical path mayinclude: optical excitation sources 3008 a and 3008 b, excitation sourcecontroller 3010, controller 3012, optical combiner 3014 (e.g. dichroiccombiner), optical switch 3018, optical fiber cables (e.g., opticalfiber cables 3016 a, 3016 b, 3016 c, 3016 d, 3016 e, 3016 f, 3016 g,3016 h, 3016 h, 3016 i, and 3016 j), optical output connectors 3020,optical input connectors 3022, optical coupler 3024 (e.g. opticalcoupler 3024 couples the optical signal from similar diameter opticalfibers into a larger diameter optical fiber), detection system 3026,sensor board 3028, and one or more sampling chambers (e.g., samplechambers 340 f, 340 g, 340 h, 340 i, etc. of FIG. 30).

FIG. 30C is a schematic of a cooling system 3036, according to anexample embodiment of the present disclosure. In this example,excitation sources 3008 a and 3008 b and detection system 3026 areprovided with an active cooling system 3036, according to an exampleembodiment of the present disclosure. Cooling of optical path componentsmay reduce optical signal interference or noise associated withdetection of optical signals by components subject to elevated thermalexposure. Any component of the optical path may be cooled individually,or any combination of components of the optical path may be cooled,including the entire optical path. In addition to cooling the opticalpath, embodiments may also be configured to cool one or more powersupplies (e.g., 3040, 3042 a, and 3042 b) of system 3000 a.

Cooling system 3036 may cool detection system 3026 and other components(e.g., excitation sources 3008 a and 3008 b) to a temperature belowambient temperature. In an embodiment, refrigeration system 3036 maycool detection system 3026 to a temperature of 100° C. below ambienttemperature. In other embodiments, other temperatures may be achievedincluding 5° C. below ambient, 10° C. below ambient, 20° C. belowambient, etc. In some embodiments, detection system 3026 andrefrigeration system 3036 may be housed in an enclosure 3038.

Refrigeration system 3036 may include any mechanism that removes heatfrom the region to be cooled. For example, cooling of the optical pathmay be accomplished through the use of thermoelectric cooling, accordingto an example embodiment of the present disclosure. Thermoelectriccooling uses the Peltier effect to create a heat flux between thejunction of two different types of materials. A Peltier cooler, heater,or thermoelectric heat pump is a solid-state active heat pump whichtransfers heat from one side of the device to the other, withconsumption of electrical energy, depending on the direction of thecurrent. Additional cooling methods may utilize liquid cooling viafluids to remove heat from components of the optical path. In certainembodiments, components of the optical path may be immersed in fluidssuch as a non-conductive mineral oil.

In other embodiments, fluids may be pumped through conduits which areoperationally coupled to components of the optical path. As the fluidsare circulated, heat of the optical path component is transferred to thefluid. The fluid may then be routed through a radiator to remove theheat. Fluid materials which may be used in liquid cooling systemsinclude: water, mineral oil, liquefied gas, etc.

With system 3000 a of FIG. 30B, fluid sampling systems may be configuredto collect optical data from multiple sources without the need for adiscrete, isolated optical path. In this example, one or more excitationsources 3008 a and 3008 b may be combined with a single detection system3026 to collect optical data from a plurality of fluid sources. Opticalexcitation sources 3008 a and 3008 b may be connected to an opticalcombiner 3014 (e.g. dichroic combiner). Optical combiner 3014 combine(or separate) multiple electromagnetic radiation emitted from excitationsources (e.g., lasers) at a 45° angle of incidence. Optical combiner3014 may be optimized to multiplex (MUX) any specific wavelength ofelectromagnetic radiation emitted from excitation sources (e.g.,lasers). Optical combiner 3014 combine may also be used to demultiplex(DEMUX) any specific wavelength of electromagnetic radiation emittedfrom excitation sources (e.g., lasers).

In one embodiment, excitation source 3008 a and 3008 b may have the sameor different excitation properties. For example, excitation source 3008a may include a laser excitation source having a wavelength of 680 nm,while excitation source 3008 b may include a laser having a wavelengthof 785 nm. In alternative embodiments excitation source 3008 a may be aninfra-red excitation source while excitation source 3008 b may be alaser excitation source having a wavelength of 785 nm. While FIG. 30Bshows two excitation sources, additional excitation sources may beemployed in alternative embodiments. In further embodiments, an opticalcombiner 3014 may be unnecessary, and therefore may be excluded.

Optical switch 3018 (e.g., see FIG. 30B) may be configured to cyclethrough a plurality of fluid sources individually via optical fibercables (e.g., optical fiber cables 3016 d to 3016 g, etc.). In analternative embodiment, optical switch 3018 may be configured to dividethe electromagnetic radiation emitted from one or more excitationsources (e.g. 3008 a and 3008 b), such that a portion of theelectromagnetic radiation is directed to each of a plurality of fluidsources via optical fiber cables.

Electromagnetic radiation transmitted from one or more excitationsources (e.g. 3008 a and 3008 b), may be delivered via optical fibercables (e.g., optical fiber cables 3016 d to 3016 g, etc.) to opticalprobes operationally coupled to a plurality of fluid sources eitherdirectly (e.g., using an immersion probe directly in the fluid source)or via a sample chamber.

Detection system 3026 may include a CCD device that may be configured todetect electromagnetic radiation emitted from a fluid source. Data maybe collected by the CCD device using a process called binning, which mayinclude line and pixel binning. Binning allows charges from adjacentpixels to be combined and this can offer benefits in faster readoutspeeds and improved signal to noise ratios albeit at the expense ofreduced spatial resolution.

A CCD includes a surface including an array of pixels at definedlocations which have the ability to receive electromagnetic radiationand convert such electromagnetic radiation into a digital signal.Electromagnetic radiation interacting the pixels along a CCD surfaceproduces an electrical charge in each pixel which may be converted intoa digital signal that may be transmitted to a computer for analysisusing software. Software may be further used to divide a CCD surfaceinto rows of pixels on a horizontal axis and/or a vertical axis. Incertain embodiments, an array of pixels may be divided into verticalrows of pixels spanning a CCD surface. In certain embodiments, an arrayof pixels may be divided into a group of vertical rows of pixelsspanning a CCD surface.

The digital signal associated with the electrical charge of each pixelmay be collected along each vertical row of pixels along the CCDsurface. The digital signal associated with each vertical row of pixelsmay be also be summed. Summation of the digital signal along one or morevertical rows of a CCD surface allows for amplification of the digitalsignal. The CCD surface may be organized into multiple regions includingone or more vertical rows of pixels. For example, a CCD having 64vertical rows of pixels may be divided into four regions of 16 verticalrows each or 32 regions of 2 rows each. In an embodiment in which theCCD surface is divided into four (4) regions having 16 vertical rows ofpixels, each row may be associated with up to four (4) different fluidsources. In such an embodiment, optical fluid data may be collected froma total of four (4) fluid sources at one time by transmittingelectromagnetic radiation received from each fluid source to acorresponding region of the CCD surface.

FIG. 31A is a flowchart illustrating a method 3100 of operating a fluidanalysis system, according to an example embodiment of the presentdisclosure.

FIG. 31B is a continuation of the flow chart of FIG. 31A, according toan example embodiment of the present disclosure. Method 3100 may be usedto operate fluid analysis systems such as systems 2000, 2000 a, 1000,100 a, 100 b, as illustrated, for example, in FIGS. 13A, 13A, 15A, 15B,17, and 18, respectively. Systems 100 a and 100 b, for example, includecooling system 302 that is described above with reference to FIGS. 17 to22. Systems 100 a and 100 b also include sampling system 304, which isdescribed above with reference to FIGS. 17, 18, and 23 to 29, andcontroller 332 that includes customized software for controllingsampling system 304 and/or cooling system 302.

In an exemplary embodiment, at stage 3102 of method 3100, controller 332of sampling system 304 may cause a processor to execute computer programinstructions (i.e., customized software) that may be stored on anon-transitory computer-readable storage device. Execution of suchcomputer program instructions may cause the controller to perform aself-diagnostics check to determine whether excitation source 344 (e.g.,see FIG. 24) and/or valves 312, 314 (e.g., see FIGS. 19 to 22) areoperational and to determine if there are any error conditionsassociated with cooling system 302 and sampling system 304 (e.g., seeFIGS. 17 and 18).

In stage 3104, the method 3100 includes performing a check to determinewhether error conditions exist. If the initial self-diagnostics checkshows error conditions, in stage 3106 controller 332 (e.g., see FIG. 23)may report these errors/failures and any related failure codes toanalytical systems 400 a and/or 400 b (e.g., see FIGS. 17 and 18). Instage 3106, controller 332 may also ensure excitation source 344 ispowered off and that all valves 312, 314 are closed (if possible).

If at stage 3104, however, controller 332 determines from the initialself-diagnostics check that no error conditions exist, then method 3100proceeds to stage 3108. In stage 3108, controller 332 initiatesoperation of sub-sampling systems 330, 330 a, 330 b, etc., by supplyingpower to sub-sampling systems 330, 330 a, 330 b, etc. (e.g., see FIGS.23 to 29).

After powering on sub-sampling systems 330, 330 a, 330 b, etc., method3100 proceeds to stage 3110. In stage 3110, method 3100 includesperforming a further self-diagnostic check to determine whether errorconditions associated with sub-sampling systems 330, 330 a, 330 b, etc.,exist. If, at stage 3110, error conditions are determined to exist, thenmethod 3100 returns to stage 3106. At stage 3106 controller 332 mayreport these errors/failures and any related failure codes to analyticalsystem 400 a and/or 400 b. At stage 3106, controller 332 may also ensurethat excitation source 344 is powered off and that all valves 312, 314are closed (if possible).

If at stage 3110, however, controller determines that no errorconditions are produced upon powering on sub-sampling systems 330, 330a, 330 b, etc., method 3100 proceeds to stage 3112. In stage 3112,controller 332 may send a signal to cooling system 302 to close thetemperature loop described above relating to action of pressure reducervalve 308, cooler 324, temperature sensor 310, and 2-way solenoid valve312, open fluid return and fluid out valves 314, and to enable cooler324, and fan 370 to cool fluid, as described above in greater detailwith reference to FIGS. 19 to 22.

Upon performing the operations described above with reference to stage3112, method 3100 proceeds to stage 3114. In stage 3114, method 3100includes performing a comparison to determine whether a measuredtemperature exceeds a predetermined threshold value. According to anembodiment, the predetermined threshold value may be taken to be 40° C.If the comparison in stage 3114 indicates that the measured temperatureexceeds the predetermined threshold (i.e., temperature >40° C.), theconditions of stage 3112 are maintained. In this regard, oil may bere-routed back to cooler 324 for further cooling.

The temperature comparison of stage 3114 may be periodically performedto determine when the measured temperature is equal to or is less thanthe predetermined temperature threshold value. For example, thecomparison of stage 3114 may be performed every few milliseconds, everysecond, every few seconds, etc. In alternative embodiments, thetemperature comparison may be performed continuously using a dedicateddigital or analog temperature comparison circuit.

If in stage 3114, the measured temperature is found be equal to or lessthan the predetermined temperature threshold (e.g., temperature <=40°C., according to an example embodiment of the present disclosure),method 3100 proceeds to stage 3116. In stage 3116, controller 33 maysend a signal to open 2-way solenoid valve (i.e., bypass valve) 312 toallow fluid through to sampling system 304 (e.g., see FIGS. 19 to 22 andrelated discussion). Upon opening bypass valve 312, method 3100 proceedsto stage 3118.

At stage 3118, once fluid has flowed into sampling system 304,controller 332 may use a length parameter to calculate an overall cycletime and to begin a timer. If there are multiple fluid sources 200 a,200 b, etc., and one source 200 a, for example, is significantly furtheraway from another source 200 b, sampling system 304 may have to cyclethe fluid for a longer time to ensure that sub-sampling system 330, 330a, 330 b, etc., is not contaminated. Once the timer has been started,method 3100 proceeds to stage 3120.

In stage 3120, controller 332 may compare a time measured by the timerwith a predetermined time threshold value. If in stage 3120, controller332 determines that the timer has not expired (i.e., that a timemeasured by the timer has not exceeded the predetermined time thresholdvalue), method 3100 proceeds to stage 3122.

In stage 3122, controller 332 may utilize sensor/transducer of inputpressure reducer valve 308 a and output pressure reducer valve 308 b ofsampling system 304 (e.g., see FIG. 23) to perform a pressure comparisonbetween the input and output pressures to determine if a sufficientpressure drop exists to identify a presence of a leak. If in stage 3122,a pressure drop indicative of a leak in the system is identified, method3100 may proceed to stage 3106. In stage 3106, controller 332 may reportthe determined potential leak/system failure and any related failurecodes to analytical system 400 a and/or 400 b (e.g., see FIGS. 17 and18). Controller may further ensure that excitation sources 344, 344 a,344 b, etc., (e.g., see FIGS. 24 to 29) are powered off and that allvalves 312, 314 are closed if possible.

At stage 3122, if the measured difference between the input and outputpressures is determined to be not significant, method 3100 returns tostage 3120. In stage 3120, the comparison between the measured time andthe predetermined time threshold value may be repeated. If in stage3120, controller 332 determines that the predetermined time thresholdvalue has not been exceeded, then method 3100 may return to stage 3122to repeat the pressure difference measurements to determine the presenceor absence. Thus, the pressure difference measurements anddeterminations of stage 3122 may be periodically repeated until thetimer indicates that the predetermined time threshold value has beenexceeded. According to an embodiment, the pressure differencemeasurements and determinations may be repeated every few milliseconds,every few seconds, etc. In further embodiments, the pressure differencemeasurements and determinations may be made continuously using adedicated digital or analog circuit.

In stage 3120, once controller 332 determines that the time measured bythe timer has exceeded the predetermined time threshold value, method3100 proceeds to stage 3124, as illustrated in the continued flowchartof FIG. 31A.

In stage 3124, controller 332 may close all valves 312, 314 to stopmovement of the fluid in sampling chamber 340, 340 a, 340 b, etc., ofsampling sub-systems 330, 330 a, 330 b, as illustrated, for example, inFIGS. 24, 25, 26, etc., respectively. Controller 332 may then initiatesampling of fluid using sampling system 304 and related sub-samplingsystems 330, 330 a, 330 b, etc., as described above, for example, withreference to FIGS. 23 to 29.

In various embodiments, sampling system 304 (e.g., see FIGS. 17, 18, and23) may perform various stages of method 3100 sequentially or inparallel. For example, in stage 3126 a, controller 332 may initiatefluid sample acquisition using sub-sampling systems 330 to 330 e, asdescribed above, for example, with reference to FIGS. 24 to 29,respectively. Controller 332 (e.g., see FIG. 23) may perform stage 3126b of method 3100 to initiate viscometer 328 to perform a viscositymeasurement of the fluid. Controller 332 may further perform stage 3126c of method 3100 to cause temperature sensors (e.g., temperature sensor310 a of FIG. 23) to measure one or more fluid temperatures. Uponperformance of stages 3126 a, 3126 b, and 3126 c, in parallel orsequentially (in any order), method 3100 proceeds to stage 3128.

In stage 3128, controller 332 may cause sampling system 304 to performmeasurements on samples of fluid. For example, sampling system 304(e.g., see FIG. 23) may cause sub-sampling systems 330 to 330 e toperform measurements as described above, for example, with reference toFIGS. 24 to 29. In stage 3128, one or more sub-sampling systems 300 to330 e may perform a plurality of measurements (i.e., acquisition of datasamples). For example, in an embodiment, sampling system 304 may causeone or more sub-sampling systems 300 to 330 e to perform a predeterminednumber of measurements. For example, subsystems 300 to 330 e may take 13to 20 samples of each respective sample type (i.e., a sample type may bea Raman measurement performed by sub-sampling system 330 a, afluorescence measurement performed by sub-sampling system 330 b, etc.),according to an example embodiment of the present disclosure. Controller332 may then receive data sets from the various sub-sampling systems 330to 330 e of sampling system 304.

Upon completion of stage 3128, controller 332 may then perform one ormore stages of method 3100 sequentially or in parallel. For example, instage 3130, controller 332 may submit sample data sets, collected bysub-sampling systems 330 to 330 e in stage 3128, to analytical system400 a and/or 400 b (e.g., see FIGS. 17 and 18).

Further, in stage 3132 which may be performed sequentially or inparallel with stage 3130, controller 332 may send a signal to coolingsystem 302 (e.g., see FIGS. 17 to 22) to end a cooling cycle. Forexample, controller 332 may cause fan 370 to terminate when cooler 324reaches an ambient air temperature as described above with reference toFIGS. 19 to 22.

Once the fluid is adequately sampled by sampling system 304, fluid maybe routed from sampling system 304 to cooling system 302. To facilitatethis return, in stage 3134 of method 3100, controller 332 may openreturn air valve 322 in cooling system 302 (e.g., see FIGS. 19 to 22) asneeded to allow air to be purged from the line and to accelerate returnof fluid if there is little or no pressure to push/gravity drain thefluid back into cooling system 302 from sampling system 304. Method 3100may then proceed to stage 3136.

In stage 3136, controller 332 may initiate a timer and may periodicallycompare a time measured by the time with a predetermined time thresholdvalue. Once controller 332 determines that time measured by the timerhas exceeded the predetermined time threshold value, method 3100proceeds to stages 3138 and 3140.

In stage 3138 controller 332 may close air valve 322 and in stage 3140,controller 332 may power off sub-sampling systems 330 to 330 e and/orsampling system 304. As described above, various stages of method 3100may be performed sequentially or in parallel. For example, as indicatedin the flow chart of FIG. 31B, controller 332 may perform stage 3130, inwhich data sets are transferred to analytical systems 400 a and/or 400 b(e.g., see FIGS. 17 and 18), in parallel with the shutdown processesrepresented by stages 3132 to 3138. Then, once stages 3130 to 3138 havebeen completed, sequentially or in parallel, controller 332 performsstage 3140 to power off sub-sampling systems 330 to 330 e and/orsampling system 304.

FIG. 32 is a flowchart illustrating a method 3200 of operatinganalytical systems 400 a and 400 b (e.g., see FIGS. 17 and 18). Further,method 3200 relates to operation of command and control systems 406 aand 406 b and databases 402 a and 402 b for respective analyticalsystems 400 a and 400 b, as described in greater detail below.Analytical systems 400 a and 400 b, command and control systems 406 aand 406 b, and/or databases 402 a and 400 b, described with reference toFIGS. 17, 18, and 32 may be implemented using apparatus, systems andmethods described herein, including various embodiments thereof.

As described above, command and control systems 406 a and 406 b may eachbe implemented as a hosted software system that may receive submittedsample data sets of measurements for fluid samples. The received datasets may then be processed through a set of machine learning models togenerate data that may be used for predictive analysis. The machinelearning models may be configured to target any type of fluid to beanalyzed. The resulting output of the sample analysis will generally bedependent on the fluid submitted, the networks processed, and thestatistical percentage accuracy for the given network model.

The output from a spectral sample is known as a spectrum. The spectrummay be visualized as a set of data points characterized by coordinates X(e.g., that may indicate wavelength, frequency, Raman shift, etc.), andY (e.g., an intensity value corresponding to the X coordinate). Graphsof data (e.g., plots of X, Y, points) may then be uploaded to analyticalsystems 400 a and 400 b where they may be stored, assessed and presentedto a machine learning model for concrete identification and systemprediction. To provide context for spectral samples, known samples maybe obtained prior to carrying out measurements on received fluid samplesso that a baseline may be established for a specific machine learningmodel. In an example, a machine learning model may include a neuralnetwork having three layers: an input layer, a hidden layer, and anoutput layer, with each layer including one or more nodes whereinformation flows between the nodes.

If the type of sample cannot be identified, machine learning models mayrequire conditioning through a “training” process. The training processmay include inputting known parameters associated with types ofsamples/sub-sampling systems 330 to 330 e, for example, to assistmachine learning models with identification of the samples and tostrengthen the resulting machine learning model. A machine learningmodel represents the knowledge of the machine learning model. Asdescribed herein, a machine learning model may be created from knowndata sets. Therefore, when a sample is submitted, the parameters forwhich the sample was collected may be required to identify theparticular machine learning model to use for identification. Forexample, a machine learning model for the fluid analysis systems (e.g.,systems 100 a, 100 b, 1000, and 2000) described herein may be defined bythe following set of parameters, including but not limited to, the typeof sub-sampling system used (e.g., 330, 330 a, etc.), the wavelength ofelectro-magnetic radiation (or if it's monochromatic), viscosity,temperature, pressure, etc.

These parameters may define the a corresponding model. Known data sets,which may include a measured spectrum corresponding to a sample of fluid(e.g., clean oil) with x ppm of y elements combined with the abovedetermined parameters may allow for “training” of a network and creationof a corresponding model. The more known (good) data that can be used totrain a machine learning model, the better the resulting model will workat identifying unknown samples. In exemplary embodiments, buildingmachine learning models may require the use of immense computationalresources. To that end, building such models may be performed byanalytical system 400 b that may be implemented in a cloud basedcomputing platform with resulting models potentially pushed to thesampling system 304 if onboard analysis is required.

In exemplary embodiments, a user may access and/or modify the analyticalsystem 400 and/or 400 b via a web application, for example, in acomputing device through any type of encrypted or unencryptedconnection. In exemplary embodiments, a user may log in to a command andcontrol system 406 a or 406 b and may access a corresponding respectivedatabase 402 a or 402 b. Access may be provided based on a user's roleand corresponding security credentials. The web application may includea graphical user interface (GUI) that may present a dashboard ofavailable sampling systems 304. The GUI may also present messages eitherpredictive analysis messages based on samples, error messages, and/ortraining request messages.

In various embodiments, the user may select a specific sampling system304, interact with the sampling system 304 and submit requests to thesampling system 304 to perform analysis and obtain a fluid sample,configure the system 304 (e.g., to setup the automated samplingtimeframe), analyze the real-time parameters coming from the system 304(e.g. temperature, last time sample taken, pressure, fluid temperature,etc.). In some embodiments, the user may also add new sub-samplingsystems 330, 330 a, 330 b, etc., to a client and/or de-authorize orshutdown existing sampling systems 330. User may also, if available,issue a software update to sampling system 304 and/or cooling system302, view analytical machine learning models and related networkstatistics, view a number of known good samples, view data related topercentage of successful identifications, and accuracy thresholds. Auser may also initiate a retraining process for a machine learning modelor request model diagnostic information.

According to an embodiment, method 3200 illustrated in FIG. 32, may beperformed as follows. In stage 3202, command and control system 406 aand/or 406 b, of respective analytical systems 400 a and 400 b, mayfirst receive submitted sample data sets of the fluid being analyzedfrom controller 332, as described above with reference stage 3130 ofmethod 3100 illustrated in FIG. 31A. Upon receipt of these sample datasets, command and control systems 406 a and/or 406 b may first retrieveclient/system information and data regarding sampling system 304configuration associated with the sample.

In stage 3204, a check may be performed to determine whether receiveddata sets are valid or if there is any error related to the process ofretrieving data sets. In the event that an error is encountered at stage3204, method 3200 proceeds to stage 3206.

In stage 3206, if the client/system information and sampling system 304configuration cannot be retrieved from the submitted sample data sets,system 400 a and/or 400 b may show a “log error” and command and controlsystem 406 a and/or 406 b may interact with corresponding respectivedatabases 402 a and 402 b (e.g., see FIGS. 17 and 18) to present thislog error to a user via a web application, for example, so that the usermay make appropriate modifications as necessary.

In the event that received data is found to be valid at stage 3204,method 3200 proceeds to stage 3208. In stage 3208, if the data is valid,command and control system 406 a and/or 406 b may submit the data setsto a model engine as a sample based on the client/system/sampling system304 configuration. In exemplary embodiments, command and control system406 a and/or 400 b may also store this sample data set in respectivedatabases 402 a and/or 402 b (e.g., see FIGS. 17 and 18) as described ingreater detail below. Method 3200 may then proceed to stage 3210.

In stage 3210, command and control system 406 a and/or 406 b may thenverify that a submission queue is available for a specific model/systemconfiguration. For example, if the sample is a type of oil with aviscosity of X, and Raman sub-sampling system 330 a (e.g., having alaser with a wavelength of 785 nm) is used to perform analysis of theoil, command and control system 406 a and/or 406 b may searchcorresponding respective databases 402 a and/or 402 b for a modelmatching those exact parameters to use in determining an identity of thesample of oil.

In the event that a submission queue is not available, method 3200 mayreturn to stage 3206. In stage 3206, system 400 may show an “log error”and command and control system 406 a and/or 400 b may interact withrespective databases 402 a and/or 402 b to present this log error to auser via a web application, for example, so that user may makeappropriate modifications as necessary.

In the event that a submission queue is available, method 3200 mayproceed to stage 3212. In stage 3212, command and control system 406 aand/or 406 b may then submit each data set to the corresponding machinelearning model. In stage 3214, machine learning model may then processresults based on each data set. Results of the processing in stage 3214may then be sent to database 402 a and/or 402 b by command and controlsystem 406 a and/or 406 b. If any issues arise with submitting each dataset to the machine learning model, method 3200 may return to stage 3206.In stage 3206, system 400 a and/or 400 b may present an “log error” touser via a GUI, for example, based on a web application.

Once fluid analysis results are processed by a machine learning model,in stage 3214, method 3200 may proceed to stage 3216. In stage 3216,command and control system 406 a and/or 406 b may notify the user ifthese results meet certain defined analysis thresholds for thesamples/type of sampling system 304. If so, method 3200 may proceed tostage 3218. In stage 3218, command and control system 406 a and/or 406 bmay end submission of the data sets to the machine learning model.

Based on the processing of results in stage 3214, command and controlsystem 406 a and/or 406 b may then determine whether machine learningmodels associated with the system require “training” in stage 3220. Ifin stage 3220, command and control system 406 a and/or 406 b determinesno training is required, method 3200 may return to stage 3218. In stage3218, command and control system 406 a and/or 406 b may end submissionof the data sets to the machine learning model.

Alternatively, in stage 3220, command and control system 406 a and/or406 b may determine that machine learning models associated with thesystem requires further training. In this event, method 3200 proceeds tostage 3222.

In stage 3222, command and control system 406 a and/or 406 b may notifythe user that appropriate training is required. In stage 3224, user maythen supply certain training inputs (e.g., via a web application) tocommand and control system 406 a and/or 406 b for each sample for whichtraining is requested. Method 3200 may then proceed to stage 3226.

In stage 3226, command and control system 406 a and/or 406 b may usethese training inputs to update/rebuild the machine learning models ormay create new machine learning models with the new data obtained fromthe fluid sample data sets. In stage 3228, command and control system406 a and/or 406 b may then store the updated/new models in database 402a and/or 402 b, and/or may deploy the updated/new models back tosampling system 304. In various embodiments, user may access existingand updated machine learning models, and related data, in database 402 aand/or 402 b via a web application, for example, as described above.

FIG. 33 is a flowchart illustrating a method 3300 of operatinganalytical systems 400 a and 400 b (e.g., see FIGS. 17 and 18) toimplement a power calibration for Raman sub-sampling system 330 a ofFIGS. 25A and 25B, according to an example embodiment of the presentdisclosure. The quality of signals received by detection system 346 a,of sub-sampling system 330 a shown in FIG. 25A, depends on the intensityof incident radiation generated by excitation source 344 a, as follows.

When radiation generated by excitation source 344 a interacts with fluidin sampling chamber 340 a, only a fraction of the incident radiationbecomes shifted in frequency and is detected as a Raman signal.Intensity of the Raman signal is, therefore, considerably less than theintensity of the incident signal. In this regard, if the incidentradiation is insufficiently intense, the resulting Raman signal will betoo weak to be detected. With increasing intensity of the incidentsignal, however, other processes such as fluorescence may begin todominate the signal and may tend to obscure the Raman signal. Because ofthese effects, it is possible to optimize the Raman signal by choosingan optimal value of the intensity of incident radiation generated byexcitation source 344 a, as described in greater detail below withreference to FIG. 33.

Method 3300, as illustrated by the flowchart in FIG. 33, describes a wayin which analytical systems 400 a and/or 400 b (e.g., see FIGS. 17 and18) may control Raman sampling sub-system 330 a (e.g., see FIG. 25A) toautomatically determine an optimal intensity of incident radiationgenerated by excitation source 344 a. As such, method 3300 provides away to calibrate sub-sampling system 330 a to generate optimal Ramansignals.

At stage 3302 of method 3300, analytical systems 400 a and/or 400 b(e.g., see FIGS. 17 and 18) may control excitation source 344 a (e.g.,see FIG. 25A) to generate a baseline intensity of incident radiation. Ingeneral, intensity of radiation generated by excitation source 344 a isgoverned the power supplied to excitation source 344 a. As such, instage 3302, analytical systems 400 a and/or 400 b set a baseline valueof power supplied to excitation source 344 a to thereby generate abaseline value of intensity of incident radiation generated byexcitation source 344 a.

In stage 3304, method 3300 includes performing Raman measurements asdescribed above with reference to FIG. 25A using an intensity ofradiation generated by excitation source 344 a resulting from thebaseline power setting fed to excitation source 344 a in stage 3302, ofmethod 3300. In this regard, radiation generated by excitation source344 a is fed to Raman probe 342 a through fiber optic cables 348 a, asshown in FIG. 25A. Radiation is then fed to sampling chamber 340 a byprobe 342 a. Radiation that is reflected/emitted from sampling chamber340 a is captured by Raman probe 342 a and is then fed to fiber opticcables 348 b. Detection system 346 a then receives the radiation fromfiber optic cables 348 b that was reflected/emitted from samplingchamber 340 a.

In stage 3306, analytical systems 400 a and/or 400 b (e.g., see FIGS. 17and 18) may evaluate the quality of signals generated by detectionsystem 346 a (e.g., see FIG. 25A) in response to receiving radiationthat was reflected/emitted from sampling chamber 340 a. For example,analytical systems 400 a and/or 400 b may determine a presence of one ormore Raman peaks in a spectrum of reflected/emitted radiation. Asdescribed above, the spectrum may be considered to be an intensity ofreflected/emitted radiation vs. frequency. For a sample of a knownmaterial, analytical systems 400 a and/or 400 b may compare a knownRaman spectrum, for the material in question, with the measuredspectrum. A degree to which the two spectra agree may be used as ameasure of the quality of the measured signal.

In stage 3308, the quality of the measured signal may be judged, byanalytical systems 400 a and/or 400 b, to determine whether anacceptable Raman signal has been obtained. As described above, if theintensity of incident radiation generated by excitation source 344 a isinsufficient, then it may be difficult to measure a Raman signal. Inthis regard, the measured signal may be dominated by background noiseand have no detectable Raman peaks. However, if the intensity ofincident radiation generated by excitation source 344 a is too great,then the measured signal may exhibit features corresponding to processesother than Raman scattering, such as features associated withfluorescence. In this regard, it is possible to determine whether theincident radiation has an intensity that is too larger or two small toproduce an acceptable Raman signal.

At stage 3308, if the measured signal is judged to be not acceptable,then method 3300 proceeds to stage 3310. In stage 3310, analyticalsystems 400 a and/or 400 b may adjust the power supplied to excitationsource 344 a. If, in stage 3308, the intensity of incident radiation wasjudged to be insufficient, then in stage 3310, analytical systems 400 aand/or 400 b may increase power supplied to excitation source 344 a tothereby increase the intensity of incident radiation generated byexcitation source 344 a. Alternatively, if in stage 3308, the intensityof incident radiation was judged to be too great, then in stage 3310,analytical systems 400 a and/or 400 b may decrease power supplied toexcitation source 344 a to thereby decrease the intensity of incidentradiation generated by excitation source 344 a. Method 3300 may thenreturn to stage 3304.

Stages 3304, 3306, 3308, and 3310, may be performed as a loop to theextent that the measured signal is judged to be unacceptable in stage3308. When implemented in hardware, firmware, or software, the loopformed by stages 3304, 3306, 3308, and 3310, may be provided with amaximum iteration parameter. For example, the maximum iterationparameter may cause the loop to exit when the loop has executed for moreiterations than the value of the maximum iteration parameter. In anembodiment, the maximum iteration parameter may be chosen to be aninteger having a value of, say, 10, 20, 50, 100, etc. In an example inwhich the maximum iteration parameter is chosen to be 50, the loopincluding stages 3304, 3306, 3308, and 3310, may termination if anacceptable Raman signal is not found in stage 3308 after 50 iterations.

If, however, in stage 3308 the measured signal is judged to beacceptable, then the above-described power calibration process is deemedto have succeeded, and method 3300 proceeds to stage 3312. In stage3312, analytical systems 400 a and/or 400 b may control Ramansub-sampling system to perform data acquisition of Raman spectra usingthe optimal value of power supplied to excitation source 344 a thatgenerates an optimal intensity of radiation.

When an acceptable Raman signal is found in stage 3308, before the loopincluding stages 3304, 3306, 3308, and 3310 has been executed for anumber of iterations not exceeding the maximum iteration parameter, thecalibration process may be said to converge. The convergence behavior ofloop 3304, 3306, 3308, and 3310 may depend on a number ofuser-adjustable parameters, such as the increment by which the powersupplied to excitation source 344 a is incremented or decremented. Upontermination, an error message may be generated, in the event that theloop does not converge before the maximum number of iterations has been.A user may then adjust one or more user-adjustable parameters to improvethe convergence of the calibration.

User-adjustable parameters may include a predetermined starting valuefor power supplied to excitation source 344 a as well as increment anddecrement values for power adjustments. For example, a starting powermay be taken to be 200 mW. Further, an example value for an incrementmay be taken to be 30 mW, and a power decrement may be taken to be 15mW. In further embodiments, the increment/decrement of power supplied toexcitation source 344 a may be chosen based on an adjustable powerwindow. Such a power window may represent an amount of power that may beincremented or decremented in a single iteration of the loop representedby stages 3304, 3306, 3308, and 3310 of method 3300 of FIG. 33. A sizeof the power window may be increased or decreased as the loop iterates.

In further embodiments, analytical systems 400 a and/or 400 b may adjustpower in increments of between 1 mW and 15 mW depending upon previousadjustments. In instances in which the signal is too strong (e.g. samplefluoresce is observed or sample is heated to boiling) analytical systems400 a and/or 400 b may lower the power on the laser by 1 mW to 15 mWdepending upon previous adjustments, and can reacquire a signal.Depending on the level of contaminants, a change of 1 mW may besufficient to acquire an acceptable signal.

Upon or after the initial Raman spectral sample is acquired, thespectral data from the calibration sample may be communicated to ananalytical controller (e.g., analytical systems 400 a and/or 400 b) andevaluated with an automatic calibration processing model, as describedin greater detail below.

An automatic calibration processing model may include one or more (e.g.,some or all) of removing all or almost all values below a defined Ramanfrequency, (e.g., 300 cm⁻¹); performing minimum and maximum scaling onthe calibration signal data values; performing a data truncation tolimit analysis to, for example, the first 200 calibration signal datavalues derived from the calibration Raman spectra; performing linearregression line-fit on the remaining calibration signal data values;determining the line y-intercept and slope values; computing residualerror sum and standard deviation values; and performing polynomialregression curve fit on the remaining data values.

Results of such a calibration processing model may be used to determineif the laser power setting was too low or too high. For example, if thelaser power level is too low, the line y-intercept value may be belowthreshold, or line slope value may be beyond an acceptable range, orpolynomial residual error sum value may be too high. Conversely, forexample, if laser power level is too high, polynomial residual error sumvalue may be too low. In certain embodiments, if the analyticalcontroller executes the calibration processing model on the calibrationspectral data and determines that the laser power is too high or toolow, the analytical controller may communicate this result to theacquisition controller.

The acquisition controller (e.g., analytical systems 400 a and/or 400 bof FIGS. 17 and 18) may then modulate the Raman laser power accordingly,as described above. If a constant power cannot be set correctly, thesystem can change into a pulsed mode with a higher power, for example,pulsing the laser on and off at a cycle of between 1 kHz and 30 kHz.During this acquisition phase, the pulse cycle and/or the laser powermay be adjusted to acquire a clean signal having little or no noise. Ifthe Raman laser power is with an acceptable range, meaning the signal isdetectable with little or no noise, the analytical controller maycommunicate this result to the acquisition controller, and downstreamsampling may be initiated.

In certain embodiments, the Raman laser may be equipped with a custompower shield which may allow the Raman laser power to be automaticallymodulated (i.e., calibrated) by an acquisition controller in conjunctionwith an analytical controller (e.g., implemented with analytical systems400 a and/or 400 b) running an automatic calibration model. In certainembodiments, the power shield may serve as an interface between computer(e.g., a Raspberry Pi processor) running calibration model and hardwareused to preform spectral analysis (e.g., using a Raman spectrometer,such as Raman sub-sampling system 330 a of FIG. 25A). In certainembodiments, the power shield may accept I2C communication from computerC and may deliver communications to 12-Bit DAC to be converted todiscretely variable DC voltage signal which may modulate laser powerlevel. The power shield may also accept various GPIO logic inputs todeliver to either the solid state relay or external relay module to turncomponents on and off.

In an embodiment, power shield may include a GPIO header interfaceddirectly with a computer GPIO header; ten position Molex header tointerface with hardware connections of Raman laser; I2C driven twelve 12Bit DAC used to control Raman laser power output level; DC-DC SolidState Relay used for switching the laser module on/off; powertransformer and rectifier used to supply the Raman laser with stable,continuous 5V DC power; six 6 Position generic header to interface withrelay module used to switch line level power to Raman spectrometer;power shield itself; and twelve Volt power supply for CAN Bus.Optionally, additional external hardware may be added. Exemplaryembodiments of additional hardware include additional sensors, satellitemodems, fiber optic cable switches, power supplies, liquid crystaldisplay LCD devices, and light emitting diode LED indicators to indicatehardware state or values. In an embodiment, hardware state indicatorsmay identify power source values related to specific states such asshut-off valve in open/closed state.

In one embodiment, the power selection process comprises three stages:upper seek, lower seek, and optimal seek. Once all three stages arecomplete, the correct power has been identified. When acquiring powerspectra, the spectrometer settings are configured to a new average countthat has been optimized to balance speed and quality for the powerselection process. Each power spectra is rated by the power ratingfunction, which estimates the amount of variation in the signal.

Power Rating Function

The power rating function calculates the mean distance between thescaled intensity curve and a smoothed version of the scaled curve. Amultiple of this value is returned as the rating.

Selection Process

The upper seek stage determines the point at which the sample starts tofluoresce. Power spectra are taken at configured intervals for example,every 30 mW until the power ratings stop changing, indicating that thesample has fluoresced. The average power rating of these fluorescedspectra is noted as the high power rating. The upper seek stagecontinues by locating the power level at which the power ratingincreases to a configured multiple of the high power rating. A binarysearch is commenced to locate the closest power level, which is chosenas the high power to complete the upper seek stage.

Lower Seek

The lower seek stage selects a low power level. A configured number ofmilliwatts is subtracted from the high power and designated as the lowpower. Power spectra are taken at the low power and at a configurednumber of consecutive power levels. The power ratings for these samplesare averaged after selection of the low power rating, and the lower seekstage are complete.

Optimal Seek

The optimal seek stage identifies the optimal power level for thesample. The optimal power rating is a configured ratio between the highpower rating and the low power rating. A binary search is used to findthe power level with a power rating ratio closest to the optimal powerrating, and this power level is returned as the optimal power.

FIG. 34 is a flowchart illustrating a method 3400 of measuring andmonitoring temperature and viscosity of a fluid/oil, according to anexample embodiment of the present disclosure. As described in greaterdetail below, trends in measured viscosity over time may provide insightinto conditions of the fluid/oil that is being monitored. For example, atrend indicating a decrease in viscosity may indicate degradation ofmotor oil. Decreasing viscosity may also indicate a presence of fuel orcoolant that has seeped into the oil and has thereby diluted the oil.Viscosity is therefore also a parameter that may be used in a fuel orcoolant dilution model of the fluid/oil under consideration, asdescribed in greater detail below. In various embodiments, a fuel orcoolant dilution model may use both Raman spectroscopy data andviscosity data to determine fuel dilution in a fluid/oil sample.

At stage 3402, method 3400 may include performing measurements oftemperature and viscosity. Temperature may be measured using atemperature sensor (e.g., temperature 5310 of FIGS. 3, 4, and 6)configured to measure a temperature of the fluid. Viscosity may bemeasured using a viscometer (e.g., viscometer 5328 of FIGS. 3, 4, and 6)configured to measure a viscosity of the fluid. In stage 3404, themethod may include sending measured temperature and viscosity data toone or more analytical systems (e.g., analytical systems 400 a and 440 bof FIGS. 17 and 18) that may process the data. Data processing, in stage3404, may include performing one or more smoothing operations on thatdata such as averaging the data over various time periods. For example,data may be averaged over every millisecond, over every second, overevery hour, over every day, over every week, etc.

At stage 3406, method 3400 may include evaluating the measured data. Forexample, evaluation in stage 3406 may include comparing measuredtemperature and viscosity data to respective temperature and viscositythresholds. Stage 3408 of method 3400 may include reporting temperatureand viscosity data. For example, if a temperature or viscosity thresholdis determined, in stage 3406, to have been exceeded, method 3400, instage 3408 a, may include issuing one or more user warnings. Since fueldilution is a common, critical failure condition for diesel engines,when fuel dilution is detected by correlation analysis, the fluidcondition monitoring system may alert a user (e.g., stage 3408 a ofmethod 3400) of a fuel dilution diagnosis in order preempt damage. Forexample, the system may suggest that the engine be taken out of serviceimmediately.

In stage 3408 b, the method may include providing temperature andviscosity data to one or more fluid dilution models (e.g., fuel and/orcoolant dilution models). In further embodiments, temperature andviscosity data may be combined with other measurements to provide inputto correlation models, as described in greater detail below. In thisregard, correlation models may be created by training machine learningalgorithms using laboratory and/or sample data to identify correlationsbetween the input data from multiple target measurements (e.g.,measurements of viscosity, temperature, oxidation, soot, iron, copper,and laser power).

In some aspects, input data from target measurements may have diagnosticvalue by itself. In this regard, correlation analysis models mayidentify correlations between multiple inputs that offer additionaldiagnostic value. For example, decreasing viscosity may, in isolation,be an indicator of high oil temperature, fuel dilution, or additivedepletion. However, correlation models may combine these input data toidentify that decreasing viscosity in combination with steadytemperature and higher trending laser power output is an indicator offuel dilution.

Summary of Analysis and Modeling Methods

Materials may be identified by their characteristic spectral signaturesin terms of peak positions and peak heights. The presence of knownmaterials in a mixture of materials may be determined by analyzingspectra for the mixture based on spectral models for known materials.For simple mixtures of a few known materials, it is possible to developmodels for the system based on first-principles chemistry and physicsmodels. However, for complex mixtures containing hundreds or thousandsof components, it may be difficult to develop models based onfirst-principles. The following disclosed modeling program producesmodels based on empirically derived, data driven approach intended tominimize introduction of bias into the modeling system. The modelingprogram, which is suitable for describing complex fluids such as motoroil containing various impurities and/or contaminants, is summarized asfollows.

Well-characterized training data may be supplied to the machine learningalgorithm as input data to generate a model. Training data may includespectroscopic data for a plurality of samples of a fluid/oil havingknown concentrations of an impurity of contaminant of interest ascharacterized by an analytical laboratory using conventional analyticaltechniques. Spectral training data may be obtained for contaminationtargets, such as fuel or coolant contamination, by producing physicalsamples having known concentrations (e.g., serial dilution) of fuel orcoolant. Degradation samples, which are positive for a specificdegradation target (e.g., soot, wear metal, etc.) may be obtained froman analytical laboratory that evaluates used oil samples thoughconventional means. Samples obtained from an analytical laboratory maybe completely characterized using a battery of conventional analyticaltechniques.

Specifics of Model Building

(1) Spectral data for a variety of well-characterized systems is used asinput to build a model for a given material in oil.

(2) The input data (i.e., training data) includes a number of knowncompositions in which the given material is present in the oil invarious concentrations.

(3) A feature selection process is performed to identify spectralfeatures (i.e., spectral peaks and corresponding frequency positions ofthe peaks) for spectra corresponding to each of the compositions.

(4) Spectral features may be characterized by a pair of quantities(f_(i),a_(i)), where f_(i) is the frequency for spectral feature “i” anda_(i) is the corresponding area-under-the curve for the given feature.The quantities (f_(i),a_(i)) may be obtained through curve fitting or bya numerical procedure performed on the input data.

(5) Important spectral features are identified as those that exhibitchanges with concentration.

(6) Although the concentration dependence is not known, changes offeature areas may be approximated with a linear model: Y=Xβ+ε, where Yis a vector of concentration values, one value for each system of theinput data set, and X is a matrix of feature area values. In this modelε represents random noise. The row index of X denotes a given system ofthe input (training) data set, and the column index denotes a frequencyvalue of a spectral feature.

(7) When the number of frequencies exceeds the number of systems, arandom lasso algorithm is used to determine the β vector.

(8) Each value of the β vector corresponds to a given frequency of aspectral feature. Larger values of the β vector correspond tofrequencies that are more important than frequencies corresponding tosmaller values of the β vector. In this sense, “more important” meansthat spectral features for these frequencies exhibit largerconcentration dependence that features for having smaller values of theβ vector.

(9) For an input data set of N systems, a number P of subsets having Msystems chosen from the N systems are considered. A β vector isdetermined for each of the P subsets.

(10) A count vector C is generated by summing all of the β vectors forthe P subsets. The largest values of the count vector determine theimportant frequencies. In this way, larger values of the vector Cindicate that the corresponding frequency was determined to be importantin more of the P subsets that frequencies having smaller values of thevector C.

(11) Frequency windows are chosen by selecting frequencies whosecorresponding entries of the C vector are above a threshold.

(12) A classifier model (i.e., a machine learning model) may beconstructed by considering the selected frequency windows to definecoordinate axes in a multi-dimensional space. The values of theareas-under-the-curve may denote coordinate values in themulti-dimensional space.

(13) A coordinate value may be defined for each frequency window bysumming or otherwise taking a suitable average of area values forspectral features in each frequency window.

(14) In this way, spectral data for each input system may be reduced toa single point in a multi-dimensional space.

(15) The classifier model may be constructed based on a separation ofclustering of data for low and high concentrations of the material. Inthis way, the multi-dimensional space is divided into a first regioncorresponding to low concentration and a second region corresponding tohigh concentration.

(16) Predictions based on the model may then be generated by reducingspectral data for an unseen system to a single data point in themulti-dimensional space, in the same way as was done for the inputtraining data. The coordinates of the data point for the unseen systemmay then be fed to the model. When data point for the unseen system isfound to be in the low/high concentration region of themulti-dimensional space, a conclusion may be drawn that the unseensystem is of low/high concentration of the material in question. In thisway, the model makes a prediction for the unseen system.

(17) The quality of the model may be assessed by constructing a“confusion matrix” that quantifies the number of correct low/highpredictions, and quantifies the number of false-high and false-lowconcentration predictions. A first confusion matrix may be generatedusing the training data using a leave-one-out cross validation strategy.A second confusion matrix may be generated using a known but unseen (bythe model) data set.

(18) The quality of the model may assessed in terms of the quantities:accuracy, precision, and recall.

Data Processing

FIGS. 35A, 35B, and 35C illustrate Raman spectroscopy data for threeconcentrations of soot in motor oil, according to an example embodimentof the present disclosure. In each of FIGS. 35A, 35B, and 35C, rawspectroscopic data is presented along with pre-processed data. Asdescribed in greater detail below, raw data is first pre-processed toremove background noise and to smooth and normalize the data.

A presence of soot in motor oil represents a level of unburned fuel inthe oil. Concentrations of soot in oil are typically denoted by aninteger in a range from 0 to 10. A value of 0 indicates no detectableconcentration of soot in oil and increasing values of the integerrepresent increasing concentrations of soot in oil. FIG. 35A illustratesRaman spectroscopy data for a “soot-0” sample, FIG. 35B illustratesRaman spectroscopy data for a “soot-3” sample, and FIG. 35C illustratesRaman spectroscopy data for a “soot-6” sample, as reported by ananalytical laboratory.

FIG. 35A presents raw data 3502, baseline signal 3504, and a signal 3506obtained by subtracting the baseline signal 3504 for the “soot-0” samplefrom the raw data 3502. Similarly, FIG. 35B presents raw data 3508,baseline signal 3510, and a signal 3512 obtained by subtracting thebaseline signal 3510 for the “soot-3” sample from the raw data 3508.FIG. 35C presents raw data 3514, baseline signal 3516, and a signal 3518obtained by subtracting the baseline signal 3516 for the “soot-6” signalfrom the raw data 3514. Further details of data pre-processing andnormalization, including determination of the baseline signal, areprovided below.

Raw optical spectroscopy data that is captured by spectroscopyinstrumentation generally has issues need to be addressed before thedata may be used. Spectroscopy data is represented as a set of datapoints which records an intensity value at each of a plurality of wavelength/frequencies. First, some of the data points should be removed dueto hardware specifications. A second issue is that the data is generallyrecorded at frequencies which are dependent on the device/spectrometerused to record the data. If these frequencies are used for building amachine learning model, then the model will only work with data that iscollected from the same device. A third issue is that the spectroscopydata generally contains a baseline component that tends to mask thesignal and may therefore be detrimental to analytical results. A forthissue is that the intensity values may vary from sample to sample due tomany physical factors. A fifth issue is that the data may contain toomuch noise and may need to be reduced using a data smoothing filter.Operations to address these issues are described below, as follows.

Before an optical spectroscopy data set may be used, it may bepre-processed to handle the above-described issues. The output of thefollowing pre-processing operations is a normalized optical spectroscopydata set that may be used for further analysis. The data smoothingoperation is optional.

The first operation removes any optical spectroscopy data that should beignored based on hardware specifications of detection equipment utilizedto collect the optical spectroscopy data. An initial signal may also beremoved. The initial signal may include data points that show an initialspike up to an initial peak value. According to an embodiment, thisinitial peak is seen at about the 300 cm⁻¹ frequency value.

The second operation uses an interpolation algorithm to transform theoptical spectroscopy data into a device-independent set of frequencyvalues. In one embodiment, a cubic spline algorithm may be used toperform the interpolation. In other embodiments, other types of 1-Dinterpolation may be used. Such other interpolation algorithms mayinclude linear, quadratic, and/or cubic splines of zero, first, secondor third order. According to an embodiment, a Savitzky-Golay filteralgorithm may be performed to smooth the data before the interpolationalgorithm is performed.

Device-independent frequencies may be computed by performing apolynomial fit between frequencies generated by two different physicalspectrometers. Performing this operation may minimize an amount ofinterpolation required for data from each of the two devices. Accordingto various embodiments, frequency differences between the twospectrometers varied from 0.017 cm⁻¹ to 62.069 cm⁻¹ at any given indexvalue. The above-described interpolation operation ensures resultingmodels preform appropriately for data obtained from any device.

The third operation removes baseline data from the interpolated opticalspectroscopy data. The baseline may be determined from the input signalusing various algorithms. In an embodiment, the baseline may bedetermined using an Adaptively Iterated Reweighting Penalized LeastSquares (airPLS) algorithm. For example, the algorithm provided in theopen-source software library “airPLS” may be used. This algorithmiteratively minimizes a penalized weighted least-square function of theform:

Q ^(t)=Σ_(i=1) ^(m) w _(i) ^(t) |y _(i) −z _(i) ^(t)|²+λΣ_(j=2) ^(m) |z_(j) ^(t) −z _(j-1) ^(t)|²,  Eq. (1)

where, z_(i) ^(t), is an approximate fit to intensity value y_(i) atiteration t. The parameter λ is an adjustable constant that dictates thestrength of the second term that acts as the penalty term. The algorithmdetermines the values of the weights w_(i) ^(t) to give a good fit tothe background signal. As such, the weights are driven to be small orzero for frequency values corresponding to intensity values y_(i)associated with peaks of the spectrum.

The weights w_(i) ^(t) of at iteration t are obtained adaptively usingthe sum of square errors (i.e., the first term in Eq. (1)) between thepreviously fitted baseline and the original signal. In order to controlthe smoothness of the fitted baseline, a penalty approach is introducedbased on sum squared derivatives (i.e., the second term of Eq. (1)) ofthe fitted baseline. The algorithm generally stops when a maximum numberof iterations is reached or a termination condition occurs.

In other embodiments, alternative algorithms may be used to determinethe background signal. For example, a polynomial fit may be performed toapproximate the background signal. Using a polynomial fit to approximatethe background signal generally requires identifying various parametersthat define the general shape of the expected input data. As such, useof polynomial fits may be error prone and less reliable than use of theabove-described airPLS algorithm.

The fourth (optional) operation of the normalization process smoothesthe data to reduce noise before data analysis is performed. According toan embodiment, a Savitzky-Golay filter algorithm may be performed tosmooth the data. The Savitzky-Golay filter algorithm fits successivesub-sets of adjacent data points with a low-degree polynomial that isderived using a linear least squares method. Use of the Savitzky-Golayfilter tends to increase the signal-to-noise ratio. Other smoothingalgorithms may be used in other embodiments.

The fifth operation performs minimum/maximum scaling on intensity valuesof respective interpolated optical data. This scaling ensures theintensity values are normalized across all samples that have beenmeasured. The data must be normalized before it may be used by themachine learning algorithms.

Models for Spectroscopy Data

As described above, spectral data may be represented as atwo-dimensional plot of intensity vs. frequency (or equivalently vs.wavelength) values. Each intensity value represents an amount ofradiation reaching a detector after incident electromagnetic radiation,generated by a source, interacts with a material. Materials may beidentified by their characteristic spectral signatures in terms of peakpositions and peak heights. The presence of known materials in a mixtureof materials may be determined by analyzing spectra for the mixturebased on spectral models for known materials. A spectral model for aknown material may be constructed as follows.

FIG. 36A shows the “soot-3” data of FIG. 35B after it has beenpre-processed and FIG. 36B shows the “soot-6” data of FIG. 35C after ithas been preprocessed, according to an example embodiment of the presentdisclosure. The data in FIGS. 36A and 36B is Raman spectroscopy datacharacterizing molecular vibrations of for soot particles in motor oil.Only some of the spectral features in FIGS. 36A and 36B are associatedwith soot particles. A model for the features associated with sootparticles may be generated by observing changes in spectral features vs.concentration of soot, as described in greater detail below.

The features shown as black shaded regions in FIGS. 36A and 36Bcorrespond to Raman scattering peaks associated with soot particles. Theappearance of three or four shaded features is a characteristic of soot.As shown in FIGS. 36A and 36B the size and shapes of the various peakschange with concentration.

FIG. 36C is a plot of a mathematical approximation to the Raman features(i.e., spectroscopic “peaks”) associated with soot, according to anexample embodiment of the present disclosure. In this example, the Ramanspectral features associated with soot may be approximated using simplemathematical functions. The curve shown in FIG. 36C is specified by asum of four Gaussian functions to give the following mathematicalapproximation:

y _(i)=ƒ(x _(i),α,β,γ),  Eq. (2)

with,

ƒ(x,α,β,γ)=Σ_(i=1) ⁴α_(i) exp(−γ_(i)(x−β _(i))²),  Eq. (3)

and where y_(i) is an intensity value corresponding to a given frequencyvalue x_(i). Table I. (below) shows values of the various parametersused in Eq. (3) to generate the curve in FIG. 36C.

TABLE I i α_(i) β_(i) γ_(i) 1 0.2 900 0.001 2 1.0 1300 0.001 3 0.5 14500.001 4 0.3 1650 0.001

The above mathematical approximation to the Raman spectroscopy data forsoot (i.e., Eqs. (1), (2), and Table I.) may be improved by performing aregression algorithm to find the best values of the parameters α, β, γ.A regression algorithm adjusts the values of parameters α, β, γ tominimize deviations of the actual data from the approximation of Eq.(2). For example, a regression algorithm seeks to find a minimum of anexpression of the form:

min{α,β,γ}∥y _(j)−ƒ(x _(j),α,β,γ)∥  Eq. (4)

where, the norm ∥y_(i)−ƒ(x_(i), α, β, γ)∥ measures deviations from eachactual intensity value y_(i) from the corresponding approximate valuepredicted by the functional form ƒ(x_(i), α, β, γ). Many differentmathematical forms may be chosen for the norm. For example, for a vectorof values z_(i), z_(i) the norm may be chosen to have the form:

∥z∥ _(p)=(Σ_(j=1) ^(N) |z _(j)|^(p))^(1/p).  Eq. (5)

A commonly used norm is given by a sum of squares,

∥z∥ ₂ ²=Σ_(j=1) ^(N) |z _(j)|².  Eq. (6)

The form of the function chosen to approximate the data, and the type ofnorm chosen, dictates the type of regression algorithm used. In theabove example, a non-linear form was chosen as shown in Eq. (3).Applying a regression algorithm using this function is therefore anon-linear regression. According to an embodiment, a non-linearleast-squares algorithm may be used to construct a curve fitting modelof the data. For example, a sum of Gaussian functions, such as given inEq. (3) may be chosen. Then, the parameters of Eq. (3) may be optimizedby an algorithm that minimizes a sum of squares such as given by Eq.(6). The optimal parameters β_(i) determine the frequency values atwhich peaks of intensity are found. The optimal parameters α_(i)determine the maximum intensity values at each of the peaks.

Therefore, collectively, the parameters (α_(i), β_(i)) serve ascharacteristic features of the spectra. Alternatively, spectral peaksmay be characterized in terms of a frequency and an area under the peak.The area under a peak that is approximated by Gaussian functions, as inEq. (3), is determined by parameters (α_(i), γ_(i)). As described ingreater detail below, it may be advantageous to work with peak positionsand areas-under-peaks (e.g., see Eq. (7) below) when determining modelsfor spectral features.

Materials may be identified by their characteristic spectral signaturesin terms of peak positions and peak heights. Thus, it is useful toconstruct mathematical models for known materials. The models for knownmaterials may then be used to determine the presence of variousquantities of the known materials in a mixture based on a measuredspectrum for the mixture and based on spectral models for the variousknown materials.

As described above, a model for spectral data may be generated usingcurve fitting. As in the example above, a functional form (e.g., Eqs.(2) and (3)) may be chosen and a computational algorithm, such as anon-linear regression algorithm, may be applied to determine parametersof the functional form. The values of the parameters therebycharacterize the spectra. Materials may be identified by theircharacteristic spectral signatures in terms of peak positions β_(i) andpeak heights α_(i). For the above example, the model of a given knownmaterial is specified in terms of the parameters (α_(i), β_(i) γ_(i)).Alternatively, for a Gaussian function, the area under the curve isgiven by (verified by straightforward integration):

$\begin{matrix}{{A_{i} = {\alpha_{i}\sqrt{\frac{\pi}{\gamma_{i}}}}},} & {{Eq}.\mspace{14mu} (7)}\end{matrix}$

thus, two of the three parameters (α_(i), β_(i) γ_(i)) are related indetermining the area A_(i) under the curve. Thus, in specifyingcharacteristics of a spectral peak, only two parameters need bespecified (β_(i), A_(i)), which specifies the peak position β_(i) (i.e.,frequency where peak is centered) and the area under the curve A_(i) ofa Gaussian function that approximates the peak.

For the soot model, described above, the spectral features includedthree or four separated peaks. In this case, it is a simple matter toguess a functional form as including four Gaussian functions. In othercases, however, it may not be easy to guess the functional form. Forexample, the spectra may have overlapping peaks as shown in the figurebelow.

FIG. 37 illustrates a mathematical function that characterizesoverlapping spectral peaks, according to an example embodiment of thepresent disclosure. The curve plotted in FIG. 37 was generated from Eq.(3) above using smaller values for γ_(i)=0.0003, which generates fourbroadened peaks. As shown, the middle two peaks partially overlap. Atypical spectral plot may have hundreds or even thousands of overlappingpeaks, making it nearly impossible to detect peak positions, heights,and widths, without using an automated algorithm.

According to an embodiment, machine learning algorithms may be used todetermine spectral features in terms of peak locations, heights, andwidths, as follows.

One way to model spectral data is through curve fitting, as describedabove. In order to fit a spectral feature, however, it is important toknow roughly where the feature is located. For example, when fitting Eq.(3) to the four peaks of the soot spectral data it is advantageous tosupply data to a regression algorithm in a neighborhood of each peak. Inthis sense, the data may be broken up into various frequency windows. Afirst frequency window may include frequency/intensity pairs (x_(i),y_(i)) in a range of frequencies roughly where the first peak islocated. Similarly, a second frequency window may includefrequency/intensity pairs (x_(i), y_(i)) in a range of frequenciesroughly where the second peak is located, etc. For this simple exampleof the soot spectra, it was easy to manually determine the variousfrequency windows. For more complicated spectra having many overlappingpeaks, it may be difficult or impossible without an automatedprocedure/algorithm.

According to an embodiment, there is a way of determining spectralfeatures in terms of areas under the curve in small frequency ranges.For example, a spectral curve may be broken up into a plurality offrequency windows, that is, small frequency ranges. The frequency rangesmay overlap. Within each frequency range, or frequency window, an areaunder the curve may be computed as a weighed sum of intensity values.Spectral features may then be determined by analysis of the areas as afunction of the average frequency of each frequency window. For example,a spectral range between two minima of the area under the curve may betaken to be a range spanning a spectral feature. This process isdescribed below with reference to FIGS. 38 to 42.

FIG. 38 illustrates a complicated spectrum 100 having multipleoverlapping peaks along with an expanded view 110 of a portion of thespectrum, according to an example embodiment of the present disclosure.Curve 100, of FIG. 38, is represented by a data set offrequency/intensity pairs. Curve 100 may correspond to measurementsperformed on one realization “s” of a material system. For example,curve 100 may represent measurements performed on a sample of motor oilhaving a first concentration of soot particles, iron based compounds, orother impurities. To characterize spectral properties of the materialvs. concentration of its constituent materials, an ensemble Σ^(S) ofsystems may be generated, with each member of the ensemble Σ^(S)corresponding to difference concentrations of a material in question(e.g., soot particles, iron, etc.).

The following analysis considers spectral properties of various systems“s” chosen from the ensemble Σ^(S) of systems. Trends among the variousmembers of the ensemble Σ^(S) may then be determined. In a first step,spectral features of a single system “s” chosen from ensemble Σ^(S) maybe analyzed. Curve 100, above, represents one such system “s” chosenfrom the ensemble Σ^(S). Spectral features of curve 100 may becharacterized in terms of peak locations and heights.

With such a complicated spectrum as curve 100, it would be difficult ifnot impossible to guess an appropriate functional form to fit thespectral data of curve 100. Therefore, an automated procedure isprovided to determine spectral features. For clarity of this example, asection 110 of curve 100 has been expanded, above, to show individualdata points.

FIG. 39 illustrates a plurality of frequency windows 130 to define areasunder the curve of FIG. 38, according to an example embodiment of thepresent disclosure. In FIG. 39, a region of frequencies 120 spanning aportion of curve 110 is shown. The region includes frequencies f₀ tof₁₄. Frequency windows 130 are defined for a plurality of overlappingfrequency regions as shown. Each frequency window is centered on anaverage frequency f₀, f₁, etc., and includes several frequencies thatspan a range including the average frequency. Within each frequencywindow, an area value may be computed. They area may be computed as aweighted sum of intensity values as a discrete approximation to anintegral of the intensity vs. frequency function over the frequencywindow. Thus, for each of frequencies f₀ to f₁₄, etc., a correspondingarea a₀ to a₁₄, etc., may be computed as shown in FIG. 40.

FIG. 40 is a table of feature area values 150 each corresponding torespective frequency values 140 according to an example embodiment ofthe present disclosure. The area values 150, as a function of frequency140, are shown graphically in FIG. 39 as open circles. As describedabove, the open circles in FIG. 39 correspond to values of area underthe curve within each frequency window. A range of frequencies betweentwo minima of the area curve may be taken to be a frequency rangespanning a spectral feature.

Such a spectral feature (i.e., a region between two minima of the areavalues) may be approximated by a mathematical function such as aGaussian, as described above (e.g., see Eqs. (2) and (3)). The intensityvalues within the identified frequency range of the spectral feature maybe supplied to a regression algorithm that may be used to obtain anoptimal fit of the spectral feature using a Gaussian function. In thisway, the whole data set of frequency/intensity pairs describing spectralcurve 100 may be fit using a series of Gaussian functions similar to thesum of functions in Eq. (3). In this way, curve 100 may be approximatedby a series of Gaussian functions having the following form,

ƒ(x,α,β,γ)=Σ_(i=1) ^(N)α_(i) exp(−γ_(i)(x−β _(i))²),  Eq. (8)

where, the upper limit of the sum N corresponds to the total number offeatures identified by analyzing area values for each frequency window130 as described above.

In some embodiments, however, it may not be practical or desirable tofit a functional form to the spectral features determined above in Eq.(8). In cases having many peaks, it may be more practical simply toidentify spectral peaks in terms of intensity values and frequencylocations, or as areas (under the curve) of peaks and frequencylocations of peaks, as described in greater detail below.

Selection of Spectral Features

A material composition may be determined based on a measured spectrum ofa mixture of materials and based on spectral models of known constituentmaterials, according to example embodiments of the present disclosure.According to an embodiment, models of constituent materials may begenerated using machine learning techniques, as follows.

In a first stage, an ensemble Σ^(S) of systems may be chosen to span aknown range of concentrations of a specific material. For example, theensemble Σ^(S) of systems may correspond to a plurality of materialshaving a range of compositions of soot particles, or iron impurities,etc. Spectral properties for each system in the ensemble Σ^(S) ofsystems include an intensity vs. frequency curve, such as curve 100,above.

The ensemble Σ^(S) of systems may be divided into various “data buckets”Σ^(S) _(σ) that may be denoted by an index σ. Each data bucket may bechosen to be a set of N spectral data sets 210 written symbolically asΣ^(S) _(σ)={Σ^(S) _(σi)}_(i=1 . . . N). Each of the N elements of thebuckets Σ^(S) _(σ) has a corresponding data set (i.e., spectral curve)including frequency/intensity pairs (x_(j), y_(j)), from which spectralfeatures (peak intensities, peak positions, peak areas, may bedetermined).

Each data bucket may be chosen based on a range of concentrations of thematerial in question. For example, a first data bucket Σ^(S) ₁ maycorrespond to a plurality of systems having compositions of the materialin question falling in a range from about 0 to 0.5%. A second databucket Σ^(S) ₂ may correspond to a plurality of systems havingcompositions of the material in question falling in a range from about0.6 to 1.0%. A third data bucket Σ^(S) ₃ may correspond to systemsystems having compositions of the material in question falling in arange from about 1.0 to 1.5%, etc. As in this example, data bucketsΣ^(S) _(σ) may be chosen to have overlapping ranges of frequencies.

For each of the systems “s” in a data bucket Σ^(S) _(σ), a spectralcurve (e.g., curve 100, above) may be analyzed to determine spectralfeatures. For example, an automated process may be carried out todetermine peak positions and peak heights based on areas under the curvecomputed in a plurality of (possibly overlapping) frequency windows, asdescribed above.

According to an embodiment, the features may simply be specified interms of peak areas. Thus, for each system s₁, s₂, . . . s_(N), in adata bucket a vector of peak area values may be computed. Each peak areavalue is associated with a corresponding frequency. The data, socomputed, may be organized as shown in FIG. 41.

FIG. 41 is a table of feature area values vs. frequency for a pluralityof systems, according to an example embodiment of the presentdisclosure. Each row of the table of FIG. 41 is labeled by systemnumbers s₁, s₂, . . . s_(N). Thus, each row of the table corresponds toareas of peak features for the corresponding system. The columns arelabeled by frequency windows.

In developing a model for a given material, one physical assumption isthat peaks corresponding to the material in question should have adependence on the concentration of the material in question. Forexample, in the plots for soot presented above, three or four peaks wereobserved to correspond to soot. It is reasonable to expect that peakheights and peak widths should change as a function of composition.Thus, in the table of FIG. 41, one would expect area values to changegoing from top to bottom in a column, if that column corresponds to apeak associated with the material in question.

It is reasonable to assume that frequencies having peak areas thatchange with composition may be associated with the material whoseconcentration is changing. Such frequencies should therefore be includedin a model of the material in question. Frequencies correspond to peaksthat do not change appreciably with concentration can reasonably beassumed to not correspond to the material in question. Such frequenciesshould therefore be excluded from consideration in a model of thematerial in question. This assumption provides a method for ranking theimportance of various features, as follows.

While the functional form of the dependence of peak areas onconcentration is not known, a simple choice is to assume a linear modelof the following form,

Y=Xβ+ε,  Eq. (9)

where Y is a vector of numbers specifying concentration, X is the abovematrix of peak area values, β is a vector of values describing thecomposition dependence of the peak area values, and ε is a vector ofconstants representing random noise. The above quantities Y and X shouldnot be confused with the (x, y) data points of a given spectral curvediscussed above.

Suppose, for simplicity, we are considering four frequencies, f₃, f₅,f₈, f₁₂, for example, and five systems s₁, s₂, . . . s₅, spanning fivedifferent composition values. In this case, the linear problem may bewritten explicitly in matrix form as follows.

$\begin{matrix}{{\begin{bmatrix}Y_{1} \\Y_{2} \\Y_{3} \\Y_{4} \\Y_{5}\end{bmatrix} = {{\begin{bmatrix}a_{1} & a_{2} & a_{3} & a_{4} \\b_{1} & b_{2} & b_{3} & b_{4} \\c_{1} & c_{2} & c_{3} & c_{4} \\d_{1} & d_{2} & d_{3} & d_{4} \\e_{1} & e_{2} & e_{3} & e_{4}\end{bmatrix}\begin{bmatrix}\beta_{1} \\\beta_{2} \\\beta_{3} \\\beta_{4}\end{bmatrix}} + \begin{bmatrix}ɛ_{1} \\ɛ_{2} \\ɛ_{3} \\ɛ_{4} \\ɛ_{5}\end{bmatrix}}},} & {{Eq}.\mspace{14mu} (10)}\end{matrix}$

where, the vector Y, and the matrix X are input parameters, and thevectors β and ε must be determined as a best fit relationship to theinput data. Parameter vectors β and ε may be determined by a regressionalgorithm, such as the Lasso algorithm. In situations in which thenumber of frequencies exceeds the number of systems, a random Lassoalgorithm may be performed to determine a suitable β vector.

In this regard, for a situation in which the number of frequencies isgreater than the number of systems, the matrix X is rectangular with thenumber of columns greater than the number of rows. As such, not allvalues of the β may be obtained. Using a ransom lasso algorithm,however, the number of frequencies is truncated to be equal to thenumber of systems so that the matrix X becomes a square matrix.Approximations to various components of β may be obtained by choosing aplurality of randomly chosen sets of frequencies for the truncation andsolving the corresponding truncated β vectors. Averaging over all thetruncated β vectors gives a suitable approximate β vector withapproximate values for all components of the β vector corresponding toall frequencies.

The determined values of the vector β characterize the importance of thecorresponding frequency value in determining concentration dependence ofpeaks at the corresponding frequency values. Suppose the vector has thefollowing values:

$\begin{matrix}{\beta = {\begin{bmatrix}0.86 \\0.23 \\0.61 \\0.12\end{bmatrix}.}} & {{Eq}.\mspace{14mu} (11)}\end{matrix}$

The above values indicate that peaks at the first and third frequencies,that is frequencies f₃, and f₈, in this example, have a strongerconcentration dependence that the other two frequencies f₅ and f₁₂. Assuch frequencies f₃, and f₈ play a larger role in determining thecomposition dependence of the model and should therefore be consideredas more important than frequencies f₅ and f₁₂.

The Lasso algorithm uses a ∥β∥₁ norm that may be written as,

∥β∥₁=Σ_(j=1) ^(N)|β_(j)|.  Eq. (12)

Use of the ∥β∥₁ norm generally has the effect of driving smallcomponents of β to zero. As such, frequencies that have correspondingcomponents of β near zero may be considered as unimportant. In this way,the relative importance of the various frequencies may be ranked.

For simplicity, it may be convenient to set the various components if βto 0 or 1 depending on whether they are below or above a threshold. Inthe above example, if the threshold were taken to be 0.5, the followingvector β would be obtained,

$\begin{matrix}{\beta = {\begin{bmatrix}1 \\0 \\1 \\0\end{bmatrix}.}} & {{Eq}.\mspace{14mu} (13)}\end{matrix}$

Equation (12) states that when the four frequencies f₃, f₅, f₈, f₁₂, areconsidered, in characterizing the concentration dependence the fivesystems s₁, s₂, . . . s₅, considered, only the first (i.e., f₃) andthird (i.e., f₈) frequencies are determined to be important.

The above determination of which frequencies are important depends onthe systems considered. In the above example, we considered only fivesystems s₁, s₂, . . . s₅, and four frequencies f₃, f₅, f₈, f₁₂. If asimilar computation were to be performed using the same four frequenciesbut with a different set of five systems s₁, s₃, s₄, s₆, s₇, the resultsregarding which frequencies are important may be different. In otherwords, the values obtained for the vector β may be different. For thisreason, according to an example embodiment of the present disclosure,many combinations of systems are chosen to generate many values of the βvector.

It may be advantageous to consider all frequencies in a spectral curve(e.g., curve 100 above) and to generate a β vector for each of a numberof groups of systems (e.g., in a data bucket Σ^(S) _(σ)).

Thus, for each system s₁, s₂, . . . s_(N), in a data bucket Σ^(S) _(σ),one may choose combinations of subsystems where each subsystem has Melements where M<N. By choosing subsystems, each having M elements, onemay construct a total of P different subsystems, where

$\begin{matrix}{P = {\begin{pmatrix}N \\M\end{pmatrix} = {\frac{N!}{{M!}{\left( {N - M} \right)!}}.}}} & {{Eq}.\mspace{14mu} (14)}\end{matrix}$

For example, starting from N=7 systems of a given data bucket (e.g.,say, data bucket Σ^(S) ₁), it is possible to generate different P=21different groups of subsystems, each having M=5 elements. For example afirst subsystem may have elements {s₂, s₄, s₅, s₆, s₇}, a second mayhave elements {s₁, s₂, s₃. s₅, s₆}, etc.

As described above, a separate vector β may be generated for each of theP subsystems, to thereby generate the set of vectors{β_(j)}_(j=1, 2 . . . P). Each vector has a number of elements equal tothe number of frequencies considered in each system. For example, ifeach system is a spectral curve (e.g., curve 100 above) having Lfrequencies, then each vector will have L components

$\begin{matrix}{\beta_{j} = {\begin{bmatrix}\beta_{j\; 1} \\\beta_{j\; 2} \\\vdots \\\beta_{jL}\end{bmatrix}.}} & {{Eq}.\mspace{14mu} (15)}\end{matrix}$

As mentioned above, each component of may be taken to be 1 or 0,respectively characterizing whether the given frequency is or is notimportant for describing the concentration dependence of the givensystem j.

For a given data bucket (e.g., say, data bucket Σ^(S) ₁), the variousfrequencies may be ranked by generating a count vector which is a givenby a component-wise summation of all the β_(j) vectors. For example, thecount vector may be defined as C=Σ_(j=1) ^(p)β_(j). This vector willhave the form,

$\begin{matrix}{{C = \begin{bmatrix}C_{1} \\C_{2} \\\vdots \\C_{L}\end{bmatrix}},} & {{Eq}.\mspace{14mu} (16)}\end{matrix}$

where, C₁ denotes the number of times the first frequency was counted asbeing important, C₂ denotes the number of times the second frequency wascounted as being important, etc., in the collection of P subsystemsgenerated from systems of the data bucket.

Thus, for a given data bucket, the elements of the count vector C may beused to rank the various frequencies. Frequencies with highercorresponding elements of the count vector C may be considered to bemore important than frequencies having smaller corresponding elements ofthe count vector C.

FIG. 43A is data plot of count values C_(j) vs. frequency values f_(j)for four data buckets corresponding to four respective ranges ofconcentrations of iron-based impurities in motor oil, according to anexample embodiment of the present disclosure. A plot such as FIG. 43Amay be used for feature selection, as described in greater detail below.

FIG. 43A was generated based on a count vector (e.g., see Eq. (16))derived from four data buckets. A first data bucket Σ^(S) ₁ includesiron concentrations of 10 to 25 ppm, a second data bucket Σ^(S) ₂includes iron concentrations of 25 to 55 ppm, a third data bucket Σ^(S)₃ includes iron concentrations of 50 to 80 ppm, and a fourth data bucketΣ^(S) ₄ includes iron concentrations of 60 to 120 ppm. In this example,data buckets Σ^(S) ₃ and bucket Σ^(S) ₄ include overlappingconcentrations.

FIG. 43B is a plot showing count values C_(j) vs. frequency values f_(j)for only the low concentration buckets Σ^(S) ₁ and Σ^(S) ₂, of FIG. 43A,according to an example embodiment of the present disclosure. The lowconcentration buckets Σ^(S) ₁ and Σ^(S) ₂ collectively span theconcentration range from about 10 to about 55 ppm. In general, plots ofthe count vector may have dissimilar features for low concentrations incomparison with those for high concentration.

FIG. 43C is a plot showing count values C_(j) vs. frequency values f_(j)for high concentration buckets Σ^(S) ₃ and Σ^(S) ₄, which collectivelyspan the concentration range from about 50 to about 120 ppm, accordingto an example embodiment of the present disclosure.

Since low and high concentration plots may have dissimilar features, itis important to consider all concentrations (i.e., all data bucketsΣ^(S) ₁, Σ^(S) ₂, Σ^(S) ₃, and Σ^(S) ₄) when choosing frequency ranges(e.g., frequency windows) in building a model to characterize thesystem.

A horizontal line may be drawn to indicate a count threshold. In thisexample, the horizontal line is chosen to have a value of one third thevalue of the largest count value. Frequency windows may be chosen byincluding only features having count values above the count threshold.

FIG. 44A is a data plot of count values generated by excluding countvalues C_(j) that fall below the threshold line for the data plot of43A, according to an example embodiment of the present disclosure. FIG.44A includes count values C_(j) for all of the data buckets. Areasonable assumption, when building a model, is to consider onlyfrequencies within frequency windows that contain the most significantpeaks.

FIG. 44B illustrates shaded regions indicating frequency windowsassociated with the peaks of FIG. 44A, according to an exampleembodiment of the present disclosure. Only features in the frequencywindows defined by the shaded regions of 44B are used to generate amodel for iron impurities in motor oil.

In this example, the frequency windows shown in FIG. 44B (as shadedregions) may be used to define a model for the material in question. Inthis regard, each frequency window may be represented as a coordinateaxis in a multi-dimension space. Spectral data for a given system may berepresented as a point in the multi-dimension space having coordinatevalues corresponding to each axis in the multi-dimensional space. Thereare several ways to define a coordinate value associated with eachcoordinate axis based on the spectral data. In one example, spectralpeaks in each frequency window may be fit with a single Gaussianfunction for each frequency window as shown in FIG. 44C.

FIG. 44C is a plot of a numerical representation of a series of Gaussianfunctions, each centered on a corresponding frequency window, accordingto an example embodiment of the present disclosure. Gaussian functionsare used here for simplicity of illustration and description. There isno restriction, however, to the use of Gaussian functions. In otherembodiments other functions (e.g., Lorentzian, Voight, and StudentTdistribution, etc.) may be used. In this example, each Gaussian functionmay be chosen to have a width that corresponds to the frequency range ofpeaks in the frequency window. The peak value of each Gaussian functionmay be chosen to be sufficiently large to enclose all of the peaks ineach frequency window. A coordinate value for each of the dimensionalaxes (i.e., each frequency window) in the multi-dimensional space may bechosen based on the corresponding Gaussian function for each frequencywindow. For example, the peak value may be chosen as the coordinatevalue. In another example, the area of the Gaussian function may bechosen as the coordinate value.

In a further example embodiment, a coordinate value may be assigned toeach frequency window by summing over the areas-under-the-curve for eachof the peaks in each frequency window. As described above with referenceto curve 100, 110, etc., for each system s₁, s₂, . . . s_(N), in a databucket, a vector of peak area values a₁, a₂, . . . a_(L), may becomputed. Each frequency window includes a certain number, Q, of peaks.A coordinate value for the frequency window may be obtained by summingthe area values for the peaks in the corresponding frequency window as:

Area_(sum)=Σ_(n=q) ₁ ^(q) ^(Q) a _(n),  Eq. (17)

where the set of integers q₁, q₂, . . . q_(Q), indexes the areasassociated with the Q peaks in each window. In general, the number ofpeaks Q in a given frequency window will vary from window to window.

FIG. 44D is a bar chart indicating a value for a sum of areas of peaksin each frequency window of FIG. 44A, according to an example embodimentof the present disclosure.

A similar bar chart may be generated for each of the samples in theensemble Σ^(S) of systems. In this way, spectral data for each system inthe ensemble Σ^(S) of systems, may be mapped into a single point amulti-dimensional space. In this example, the single point for a givensystem would be represented by the coordinate values {a₁, a₂, a₃, a₄,a₅, a₆} (area sums) along the various axes (i.e., correspondingfrequency windows) in the multi-dimensional system (i.e.,six-dimensional system in this example).

Machine learning techniques may then be used to generate a model of theensemble Σ^(S) of systems by observing trends in the distribution ofpoints in the multi-dimensional space, as described in greater detailbelow.

Machine Learning—Model Building

According to an embodiment, models for materials may be built usingmachine learning techniques based on a feature selection process, asdescribed above. Generally, in the domain of machine learning there isan emerging practice called “deep learning,” which may refer to a one ormore specific machine learning algorithms including artificial neuralnetworks (ANN), random forests, support vector machines (SVM) andnon-negative matrix factorization (NMF). Thus, deep learning may includea plurality of algorithms that tend to exhibit similar characteristics.Deep learning systems are ANN that are constructed with multiple layers,sometimes called multi-level perceptrons. Use of advancing computationaltechnology, such as graphical processing units, parallel processing, andmulti-threading, as well as larger training data sets further empowerdeep learning to provide advance diagnostic and predictive insight.

Exemplary data models for evaluating Raman spectral data include modelsfor oxidation, soot, fuel dilution, silicon, wear metals, and coolant.Generally, these models are trained to recognize specific Raman spectralpatterns that allow for determination of the specific target. Suchmodels may be used to identify chemical groups associated within anapproximate Raman wave number range. The chemical name of the groupcommon to a particular wavenumber range, and the intensity of the groupwithin a particular wavenumber range, may be identified.

In an embodiment, a Support Vector Machine model may be used to generatea material model based on significant frequencies/intensities, asfollows. Classifier models take a plurality of data points as input.Each data point may be considered to be a point in a multi-dimensionalspace. A model of the data is constructed on the assumption that thatdata may be classified into two or more categories. In the simplest ofsuch methods, data is classified into one of two categories. The term“machine learning” means that the model is automatically constructed bya computer (i.e., by a computational “machine”). Since data points areviewed as points in a multi-dimensional space, a classifier model may bedetermined if the data corresponding to the two categories is found tolie in distinct regions of the multi-dimensional space.

According to an embodiment, a machine learning model may be constructedfor spectral data. In this regard, significant frequencies, determinedas described above, serve as coordinates in the multi-dimensional space.Corresponding values associated with spectral peaks in correspondingfrequency windows may serve as values along the various dimensions. Asdescribed above, the values may correspond to peak intensities, sums ofareas-under-the-curve, as peak values associated with a curve fit to aplurality of peaks in a given window, etc.

In a simple example, a model may be constructed by considering only twofrequencies. More precisely, two frequency ranges may be specifiedcorresponding to portions of a spectral data set in which significantspectral peaks have been determined. The two frequency ranges may bethought of as two coordinate axes spanning a two dimensional space. Thecorresponding peak areas (i.e., sum of areas for each peak in eachfrequency window) may therefore be thought of as coordinates in the twodimensional space.

FIG. 45 is an illustration of possible distribution of peak areascorresponding to a two-dimensional model, according to an exampleembodiment of the present disclosure. In this example, data tends toreside in one of two clusters residing in two respective regions. Amachine learning algorithm may be used to generate a mathematicaldescription of the distribution of points in the multi-dimensional space(i.e., two dimensions in this example). In this regard, the solid linein FIG. 45 above represents a hyperplane that best separates the twoclusters of data. The dashed lines indicate a separation region betweenthe two classes of data points.

In this example, the hyperplane is represented as a linear function andis determined by the algorithm by finding coordinates of the hyperplanethat maximizes the distance of each point in the space from thehyperplane. In other embodiments, other functional forms for the hyperplane may be used. For example, in certain embodiments, a non-linearfunction may be used to generate a hyperplane having a curved surface.

According to an embodiment, training data corresponds to two classes ofdata. In the example of spectral data, the two classes may correspond tolow concentration and high concentration of a material in question. Aclassification algorithm may then generate the hyperplane, describedabove, and may represent the hyperplane as a mathematical function(i.e., linear function, non-linear function, etc.). The model may thenbe used for prediction of properties of unknown materials, as follows.

Well-characterized training data may be supplied to the machine learningalgorithm as input data to generate a model. Training data may includespectroscopic measurements for a plurality of samples of a fluid/oilhaving a known concentration of an impurity of contaminant of interestas characterized by an analytical laboratory using conventionalanalytical techniques.

Spectral training data may be generated for use in models that identifycontamination targets such as fuel or coolant contamination, byproducing physical samples having known concentrations (e.g., serialdilution) of fuel or coolant. Spectral data from each known sample maybe collected and used as a spectral training data set to train models toidentify corresponding contamination targets.

In another embodiment, spectral training data may be generated for usein models that identify degradation targets. When the specific fluid isan oil, such as motor oil, degradation products may include soot, wearmetals, oxidation products, and the like. In the case of engine oil,degradation targets such as soot, wear metals, and oxidation productsarise due to breakdown of engine oil through use and/or may arise due toengine wear. Degradation samples, which include a specific degradationtarget (e.g., a known concentration of soot, wear metal, etc.) may beobtained from an analytical laboratory that evaluates used oil samplesthough conventional means. Samples obtained from an analyticallaboratory may be completely characterized using a battery ofconventional analytical techniques. Spectral training data may becollected from used engine oil samples, which may be fully characterizedby a conventional analytical laboratory, for use in models that identifydegradation targets such as soot, wear metals, oxidation products, andthe like.

Physical samples characterized as soot-positive may also include aconcentration of soot. Obtaining spectral training data over a range ofknown soot concentrations may allow quantification of soot concentrationof unknown samples via regression and/or classification analysis togenerate quantitative or semi-quantitative models for soot, wear metals,oxidation state, and the like. As mentioned above, soot concentration,as reported by conventional analytical laboratories, is expressed usingdimensionless units.

For example, concentrations of soot in oil are typically denoted by aninteger in a range from 0 to 10. A value of 0 (i.e., “soot-0”) indicatesno detectable concentration of soot in oil. Increasing values of theinteger (i.e., “soot-1,” “soot-2,” etc.) represent increasingconcentrations of soot in oil. In some embodiments, a soot concentrationabove 4 may indicate a dangerous operating condition for an engine. Insome embodiments, disclosed systems may provide a critical operatingcondition warning or shut-down procedure that may be implemented when asoot concentration above 4 is detected, as described in greater detailbelow.

An unknown material may be processed to generate data in the same way aswas done for the training data. For example, spectral features of theunknown material may be analyzed in each of the spectral windowscorresponding to the training data. In this sense, spectral data for theunknown material may be represented as a single point in the samemulti-dimensional space. That single point may then be classified ascorresponding to one or the other of the two classes represented by themodel. The result is a prediction from the model that the unknownmaterial falls into one or the other of the two classes. In the exampleof spectral data having low and high concentration of a material inquestion (e.g., iron, soot, etc.) the result is a prediction that thematerial corresponds to either high concentration or low concentration.

A plurality of models may be generated to give more generalconcentration information regarding a material in question. For example,a model may predict a material to have a concentration above or below 1%of the material in question. A second model may predict a material tohave a concentration above or below 2% of the material in question. Athird model may predict a material to have a concentration above orbelow 3% of the material in question, etc. Concentration may beexpressed as a percent for some materials (e.g., soot) and may beexpressed in ppm (i.e., parts per million) for other materials (e.g.,iron, etc.).

Model Testing and Prediction

Disclosed embodiments may be used to generate a model for an motorcoolant in engine oil. As an engine operates, various impurities maymake their way into the oil and thus contaminate the oil. One suchcontaminant may be ethylene glycol that is a component of enginecoolants. A model for coolant in oil was developed using theabove-described techniques, according to an example embodiment of thepresent disclosure.

FIG. 46 is a data plot of Raman spectral data of pure ethylene glycolaccording to an example embodiment of the present disclosure. Thefrequency distribution of peaks for pure ethylene glycol (shown in FIG.46) gives an indication of what frequency ranges may be important for amodel of a mixture of ethylene glycol and motor oil. To generate themodel, an ensemble Σ^(S) of systems spanning a plurality ofconcentrations of ethylene glycol in oil was generated. Spectral datawas measured for each of the ensemble Σ^(S) of systems and a pluralityof data buckets Σ^(S) _(σ) was defined. Feature selection operationswere performed, as described above, to determine the most importantpeaks to use in the model.

FIGS. 47A, 47C, and 47E are plots of count values C_(j) vs. frequencyvalues f_(j) for low, medium, and concentrations of coolant in motoroil, respectively, obtained using a first laser that generates incidentradiation of wavelength of 680 nm. FIGS. 47B, 47D, and 47F are plots ofcount values C_(j) vs. frequency values f_(j) for low, medium, and highconcentrations of coolant in motor oil, respectively, obtained using asecond laser that generates incident radiation of wavelength of 785 nm,according to an example embodiment of the present disclosure.

FIG. 48A is a box plot illustrates a distribution of sums of peak areasfor important frequency windows for coolant in motor oil, according toan example embodiment of the present disclosure. This plot illustratesthe most important four frequency windows that were identified based onthe data of FIGS. 47A to 47F. The frequency windows span a region offrequencies centered on 874 cm⁻¹, 1052 cm⁻¹, 1186 cm⁻¹, and 1472 cm⁻¹.These frequency windows were identified from measurements made using alaser having a wavelength of 785 nm. In this example, a first frequencywindow spans a range from about 860 cm⁻¹ to about 889 cm⁻¹, the secondfrequency window spans a range from about 1043 cm⁻¹ to about 1061 cm⁻¹,the third frequency window spans a range from about 1173 cm⁻¹ to about1200 cm⁻¹, and the fourth frequency window spans a range from about 1464cm⁻¹ to about 1481 cm⁻¹.

FIG. 48A is inter interpreted as follows. The horizontal axis indexesthe frequency windows and the vertical axis plots the values of areasums for various systems. In this regard, for each system, a value isgenerated for the area sum for all peaks in given frequency windowaccording to Eq. (17). For each system, one sum according to Eq. (17) iscomputed for each frequency window for low concentrations and one sumaccording to Eq. (17) is computed for each frequency window for lowconcentrations. Thus, since there are four frequency windows in thisexample, each system in the ensemble Σ^(S) of systems is characterizedby eight data points expressed as area sums: four area sums for lowconcentrations, each one corresponding to a respective frequency window,and four area sums for high concentration, each one corresponding to arespective frequency window.

A scatter plot is generated by plotting the results for all of thesystems in the ensemble Σ^(S) of systems. The resulting scatter plot hasarea sums plotted along the vertical axis. For clarity, each of thescatter plots along the vertical axis is displaced somewhat along thehorizontal axis. Since all of the peaks in each frequency window havebeen summed, there is no significance to the horizontal axis other thanto indicate roughly the frequency window to which each vertical scatterplot of area values belongs. In this sense, the width of each box in thefigure above is not significant. The height of each box, however,illustrates where most of the area sum points are distributed (i.e.,within one standard deviation) for the ensemble Σ^(S) of systems. Thevertical error bar (having larger width) indicates two standarddeviations.

FIG. 48B is a violin plot showing the distribution of sums of peak areasof FIG. 48A, according to an example embodiment of the presentdisclosure. As described above, for each system there are four points(i.e., area sums according to Eq. (16)) for low concentrations and fourpoints for high concentration. Each of the four points for low and highconcentrations represents an area sum for a corresponding frequencywindow. Thus, only the placement along the vertical axis hassignificance. The placement along the horizontal axis merely indexes thecorresponding four frequency windows. In this example, the width of theshaded region for each point, for a given value of the vertical axis,indicates a relative number of systems having the given value of thearea sum (i.e., the value along the vertical axis).

As described above, the data for area sums vs. frequency may be used astraining data for a machine learning model. For the resulting model tobe effective, it is advantageous for the data to be not appreciablyoverlapping in the four dimensional space spanned by the determined fourfrequency windows, as illustrated in FIG. 49, and described below.

FIG. 49 plots the data of FIGS. 48A and 48B projected onto the varioustwo-dimensional planes so that the distribution of area sums for low andhigh concentrations of coolant in motor oil may be investigatedvisually, according to an example embodiment of the present disclosure.FIG. 49 is interpreted as follows. Using the techniques described above,spectral data for each system has been reduced to a single data point{a₁, a₂, a₃, a₄} in a four dimensional space. The coordinate axes in thefour dimensional space represent the frequency windows centered onfrequencies 874 cm⁻¹, 1052 cm⁻¹, 1186 cm⁻¹, and 1472 cm⁻¹. Therefore,for a given system, a₁ represents the area sum of all peaks in thefrequency window centered around 874 cm⁻¹, a₂ represents the area sum ofall peaks in the frequency window centered around 1052 cm⁻¹, a₃represents the area sum of all peaks in the frequency window centeredaround 1186 cm⁻¹, and a₄ represents the area sum of all peaks in thefrequency window centered around 1472 cm⁻¹.

Clustering of data may be seen in FIG. 49 by observing plots of variouspairs of coordinates. For example, the graphs in the bottom row,starting from the left, plots {a₁, a₄} values, {a₂, a₄} values, and {a₃,a₄} values. The last graph on the bottom right plots histograms for areavalues for respective low and high concentrations for the variable a₄.The rest of the data may be interpreted similarly. The row that issecond from the bottom, starting from the left, plots {a₁, a₃} valuesand {a₂, a₃} values. The third graph is a histogram for variable a₃, andthe last plot on the right, is a plot of {a₄, a₃} values. The collectionof plots in FIG. 49 is symmetric about the diagonal from the top left tothe bottom right, as it should be. FIG. 49 indicates that there is goodseparation between clusters of area sum values for low concentrationsand high concentrations. As such, one may suspect that this data may bedescribed by a machine learning model.

A machine learning model for coolant in oil was generated using aSupport Vector Machine algorithm using the above plotted data sets asfollows. For an ensemble Σ^(S) of systems containing N systems, a subsetof the N systems was used as training data to generate the machinelearning model. The model was then tested on the remaining systems.

FIG. 50 illustrates results obtained from a Support Vector Machine modelof coolant in motor oil, according to an example embodiment of thepresent disclosure. These results obtained for an ensemble of 100testing systems. In this example, 86 of the systems were correctlypredicted to be high concentration systems.

FIG. 50 presents data in the form of a “confusion matrix” thatcharacterizes the quality of the models for coolant in motor oil builtaccording to the above-described process. The values along the diagonalindicate how many predictions were correct. The index values 0 and 1correspond to low concentration and high concentration of coolant inoil. Thus, the 0-0 element is the number N₀₀ of times a lowconcentration sample was correctly predicted to be a low concentrationsystem. Similarly, the 1-1 element indicates the number N₁₁ of times ahigh concentration was correctly predicted to be a high concentrationsystem. The off diagonal elements indicate the number N₀₁ of false highconcentrations (i.e., a low concentration system incorrectly predictedto be a high concentration system) and the number N₁₀ of false lowconcentrations (i.e., a high concentration system incorrectly predictedto be a low concentration system, respectively).

Three metrics: (1) accuracy, (2) recall, and (3) precision, may be usedto quantify the quality of the model. Accuracy is defined as:

$\begin{matrix}{{{acccuracy} = \frac{N_{11} + N_{00}}{\left( {N_{11} + N_{10} + N_{01} + N_{10}} \right)}},} & {{Eq}.\mspace{14mu} (17)}\end{matrix}$

that is, the ratio of the total number (N₁₁+N₀₀) of correct predictionsto the total number N₁₁+N₀₀+N₀₁+N₁₀ of predictions.Precision is defined as:

$\begin{matrix}{\frac{N_{11}}{N_{11} + N_{01}},} & {{Eq}.\mspace{14mu} (18)}\end{matrix}$

that is, the ratio of the total number N₁₁ of correctly predicted highconcentration systems divided by the sum of the number N₁₁ of correctlypredicted high concentrations and the number of false highconcentrations N₀₁ (i.e., systems that are low concentration but areincorrectly predicted to be high concentration systems).

Recall is defined as:

$\begin{matrix}{\frac{N_{11}}{N_{11} + N_{10}},} & {{Eq}.\mspace{14mu} (19)}\end{matrix}$

that is, the ratio of the total number N₁₁ of correctly predicted highconcentration systems divided by the sum of the number N₁₁ of correctlypredicted high concentrations and the number of false low concentrationsN₁₀ (i.e., systems that are high concentration but are incorrectlypredicted to be low concentration systems).

The better the model is at describing the system in question, the higherwill be the values of the various metrics: accuracy, precision, andrecall. The above results, illustrated in the confusion matrix, showthat the model generates good predictions for systems having both highand low concentrations of coolant in oil.

A similar model for fuel in oil was developed using the above-describedtechniques, according to an example embodiment of the presentdisclosure. As described above, fuel is one of the various contaminantsthat may make its way into engine oil as the engine operates due toleaks in various gaskets/seals.

To generate the model, an ensemble Σ^(S) of systems spanning a pluralityof concentrations of fuel in oil was generated. Spectral data wasmeasured for each system “s” of the ensemble Σ^(S) of systems and aplurality of data buckets Σ^(S) _(σ) was defined. In this example, fivedata buckets Σ^(S) ₁, Σ^(S) ₂, Σ^(S) ₃, Σ^(S) ₄, and Σ^(S) ₅ weredefined. The five data buckets were chosen to have the followingoverlapping concentrations (1) 0.0% to 2.0%, (2) 0.5% to 2.5%, (3) 1.0%to 3.5%, (4) 2.0% to 5.0%, and (5) 4.0% to 20%, respectively.

Further, for feature selection and model building, the above-describedconcentrations were considered to span three categories: low, medium,and high concentrations. For example, low concentration systems arerepresented by data bucket Σ^(S) ₁, medium concentration systems arerepresented by data buckets Σ^(S) ₂, Σ^(S) ₃, and Σ^(S) ₄, and highconcentration systems are represented by data bucket Σ^(S) ₅. Featureselection operations were performed, as described above, to determinethe most important peaks to use in the model for fuel in oil. Featuresdetermined for fuel in motor oil are described below with reference toFIGS. 51A to 51F.

FIGS. 51A, 51C, and 51E are plots of count values C_(j) vs. frequencyvalues f_(j) for low, medium, and concentrations of fuel in motor oil,respectively, obtained using a first laser that generates incidentradiation of wavelength of 680 nm. FIGS. 51B, 51D, and 51F are plots ofcount values C_(j) vs. frequency values f_(j) for low, medium, and highconcentrations of fuel in motor oil, respectively, obtained using asecond laser that generates incident radiation of wavelength of 785 nm,according to an example embodiment of the present disclosure.

By performing the feature selection operation, as described above, fourfrequency windows were identified spanning regions near 2572 cm⁻¹, 3141cm⁻¹, 3466 cm⁻¹, and 4117 cm⁻¹. These frequency windows were obtainedfrom measurements using incident radiation having a wavelength of 680nm. The data for high and low concentrations was found to generally liein separated regions of a multi-dimension space spanning four dimensionscorresponding to the four frequency windows.

FIG. 52A is a box plot that illustrates a distribution of sums of peakareas for important frequency windows for fuel in motor oil, and FIG.52B is a violin plot that illustrates another way of viewing thedistribution of sums of peak areas of FIG. 52A, according to an exampleembodiment of the present disclosure.

As described above, for each system of FIGS. 52A and 52B there are fourpoints (i.e., area sums according to Eq. (17)) for low concentrationsand four points for high concentration. Each of the four points for lowand high concentrations represents an area sum for a correspondingfrequency window. Thus, only the placement along the vertical axis hassignificance in FIGS. 52A and 52B. The placement along the horizontalaxis merely indexes the corresponding four frequency windows. In FIG.52B, the width of the shaded region for each point, for a given value ofthe vertical axis, indicates a relative number of systems having thegiven value of the area sum (i.e., the value along the vertical axis).

As described above, the data for area sums vs. frequency may be used astraining data for a machine learning model. For the resulting model tobe effective, it is advantageous for the data to be not appreciablyoverlapping in the four dimensional space spanned by the determined fourfrequency windows.

FIG. 53 plots the data of FIGS. 52A and 52B projected onto the varioustwo-dimensional planes so that the distribution of area sums for low andhigh concentrations of fuel in motor oil may be investigated visually,according to an example embodiment of the present disclosure.

As described above, for the model of fuel in oil, clustering of data maybe seen by observing plots of various pairs of coordinates. For example,the graphs in the bottom row, starting from the left, plots {a₁, a₄}values, {a₂, a₄} values, and {a₃, a₄} values. The last graph on thebottom right plots histograms for area values for respective low andhigh concentrations of fuel in motor oil for the variable a₄. The restof the data may be interpreted similarly.

The row that is second from the bottom, starting from the left, plots{a₁, a₃} values and {a₂, a₃} values. The third graph is a histogram forvariable a₃, and the last plot on the right, is a plot of {a₄, a₃}values. The above collection of plots is symmetric about the diagonalfrom the top left to the bottom right, as it should be. The above plotsindicate that there is good separation between clusters of area sumvalues for low and high concentrations of fuel in motor oil. As such,one may suspect that this data may be described by a machine learningmodel.

A machine learning model for fuel in oil was generated using a SupportVector Machine algorithm using the above plotted data sets as follows.For an ensemble Σ^(S) of systems containing N systems, a subset of the Nsystems was used as training data to generate the machine learningmodel. The model was then tested on the remaining systems.

FIG. 54 illustrates results obtained from a Support Vector Machine modelof fuel in motor engine oil, according to an example embodiment of thepresent disclosure. FIG. 54 presents data in the form of a confusionmatrix that characterizes the quality of the models for fuel in motoroil built according to the above-described process. The results of FIG.54 show that the model generates good predictions for systems havingboth high and low concentrations of fuel in motor oil.

A model for soot in oil was developed using the above-describedtechniques, according to an example embodiment of the presentdisclosure. Soot is one of the various contaminants that may make itsway into engine oil as the engine operates due and is a by-product offuel that is incompletely burned by the engine.

To generate the model, an ensemble Σ^(S) of systems spanning a pluralityof concentrations of soot in oil was generated. Spectral data wasmeasured for each system “s” of the ensemble Σ^(S) of systems and aplurality of data buckets Σ^(S) _(σ) was defined. In this example, fivedata buckets Σ^(S) ₁, σ^(S) ₂, Σ^(S) ₃, Σ^(S) ₄, Σ^(S) ₅, and Σ^(S) ₆were defined. The six data buckets were chosen to have theconcentrations specified as (1) soot-2, (2) soot-3, (3) soot-4, (4)soot-8, (5) soot-9, and (6) soot-10, respectively, where the integers insoot-2, soot-3, etc., is a conventional measure of soot concentration.

Further, for feature selection and model building, the above-describedconcentrations were considered to span two categories: low and highconcentrations. For example, low concentration systems are representedby data buckets Σ^(S) ₁, Σ^(S) ₂, Σ^(S) ₃, while high concentrationsystems are represented by data buckets Σ^(S) ₄, Σ^(S) ₅, Σ^(S) ₆.Feature selection operations were performed, as described above, todetermine the most important peaks to use in the model for soot in oil.Features determined for soot in motor oil are described below withreference to FIGS. 55A to 55D.

FIGS. 55A and 55B are data plots of count values C_(j) vs. frequencyvalues f_(j) for low and high concentrations of soot in motor oil,respectively, obtained using a first laser that generates incidentradiation of wavelength of 680 nm. FIGS. 55C and 55D are data plots ofcount values C_(j) vs. frequency values f_(j) for low and highconcentrations of soot in motor oil, respectively, obtained using asecond laser that generates incident radiation of wavelength of 785 nm,according to an example embodiment of the present disclosure.

By performing the feature selection operation, as described above, fivefrequency windows were identified spanning regions near 417 cm⁻¹, 523cm⁻¹, 947 cm⁻¹, 1365 cm⁻¹ and 1914 cm⁻¹. These frequency windows wereidentified based on measurements made using incident radiation having awavelength of 785 nm. Unlike the situation for the coolant in oil, andfuel in oil models, data for high and low concentrations of soot in oilwas found to overlap a multi-dimension space spanning five dimensionscorresponding to the five frequency windows as shown in FIG. 56.

FIG. 56 plots the data of FIGS. 55A to 55D projected onto the varioustwo-dimensional planes so that the distribution of area sums for low andhigh concentrations of fuel in motor oil may be investigated visually,according to an example embodiment of the present disclosure. In thisexample, the projection to five coordinates, one for each of the fivefrequency windows may have led to cancellations of importantinformation. For example, suppose areas associated with some spectralfeatures in a frequency window are found to decrease with increasingconcentration and others are found to increase with increasingconcentration. In such a situation, summing the area values, as was donefor the models of coolant and fuel in oil may obscure the changes. Assuch, a Support Vector Machine model may not be appropriate to use as amodel of soot in oil.

FIG. 57 is a box plot that illustrates a distribution of sums of peakareas for important frequency windows for soot in motor oil, accordingto an example embodiment of the present disclosure. This plot shows thatarea sums are nearly the same for low and high concentrations of soot inoil.

According to an example embodiment of the present disclosure, toovercome the above difficulties, a model of soot in oil was constructedusing a decision tree methodology. In this model, all spectral featureswithin each window of important frequencies were retained. Favorableresults were obtained using this decision tree approach as shown in theconfusion matrix of FIG. 58.

FIG. 58 illustrates results obtained from a decision tree model of sootin motor oil, according to an example embodiment of the presentdisclosure. FIG. 58 is a confusion matrix generated by training datasets using leave-one-out cross-validation. The results from these testsare promising for the soot in oil model.

Summary of Modeling and Analytical Methods

FIG. 59 is a flowchart that summarizes data analysis and methodsemployed herein, according to an example embodiment of the presentdisclosure. In stage 6002, method 6000 includes determiningspectroscopic data for a physical system. According to an exampleembodiment of the present disclosure, the spectroscopic data may beobtained by performing Raman spectroscopy measurements on a physicalsystem. The system may be chosen to be one of an ensemble of systemsspanning a range of concentrations of a material of interested inanother material. For example, the material of interest may be soot,iron, copper, fuel, coolant, etc., in motor oil. The spectroscopic datamay include a plurality of frequency/intensity pairs. Each data pointmay characterize an intensity of electromagnetic radiation, of aspecific frequency, reaching a detector after interacting with thephysical system.

Method 6000 may include operations to process spectroscopic data foreach system of an ensemble of systems. Stage 6003 may includingfiltering the data to smooth the data by removing high frequency noisecomponents. As described above, a Savitzky-Golay filter algorithm may beperformed to smooth the data. Stage 6004 may include interpolation ofdata points to generate device-independent Raman frequencies, asdescribed above. Method 6000 may further include, at stage 6006, atruncation procedure to remove artifacts caused by the measuring device.Such data points should be removed to avoid mischaracterizing thephysical system in question. At stage 6008, method 6000 includedcomputing and removing a baseline signal. At stage 6010, method 6000 mayinclude scaling to data to force data from all samples within anensemble of systems to lie within a range of values defined by apredetermined minimum and maximum. According to an embodiment, theminimum may be taken to be 0 and the maximum may be taken to be 1.

At stage 6012, method 6000 may include determining areas under the curvefor small frequency windows. In some embodiments, the frequency windowsmay be overlapping. At stage 6014, method 6000 may include performingfeature selection and feature scoring operations. Operations in stage6014 are performed to determine features that are most important forbuilding a model of the system in question.

In an embodiment, features may be determined to be important based ontheir dependence on concentration of a material in question. Forexample, various spectral features may be observed to change withincreasing concentration of a given impurity or contaminant (e.g., fuel,coolant, soot, iron, copper, etc.), while other spectral features may beobserved to have little concentration dependence. In an embodiment, alinear model by be used to characterize concentration dependence ofspectral features. Results of the linear model may be used for featureselection and feature ranking.

In stage 6016, method 6000 may include generating a machine learningmodel of the system. In this sense, spectral data from a plurality ofsystems chosen from an ensemble of systems may be characterized in termsof coordinates in a multi-dimensional space. Each frequency that isdetermined to be important in stage 6014 may be taken to define acoordinate direction in a multi-dimensional space. Values of spectralfeatures may be taken as coordinates in the multi-dimensional space. Forexample, peak heights or peak areas may be taken to serve as coordinatesin the multi-dimensional space. In other embodiments, a coordinate maybe generated by summing peak areas or by choosing a peak height of afunctional form fitted to encompass a plurality of spectral features ina frequency window.

A machine learning model may then be constructed based on a collectionof training data. For each system in the collection of training datasets, spectral data may be characterized as a single point in amulti-dimensional space, as described above. Classifier models may thenbe generated when the data exhibits favorable clustering behavior in themulti-dimensional space. In stage 6018, classifier models may be used togenerate binary predictions regarding properties of various systems. Forexample, a classifier model may be generated for a given threshold ofconcentration of a contaminant or impurity in another material. Forexample, a model of soot may be generated that allows a prediction foran unseen material. In this regard, an unseen material may be predictedto have a concentration that is above or below a predeterminedthreshold.

In certain situations, a Support Vector Machine model that is generatedbased on training data sets provides a suitable characterization ofspectral data for unseen systems. For example, models of fuel andcoolant in oil are well characterized by a Support Vector Machine mode.In other embodiments, various other machine learning models may be moresuitable. For example, a decision tree model provides a bettercharacterization of soot in oil than does a Support Vector Machinemodel.

Predictive Analytics, Fluid Condition Diagnosis, and System ControlFeedback

Application of disclosed models may provide methods of predictiveanalytics and diagnosis of complex fluid conditions. Such methods mayallow preventive measures to be taken (e.g., by an operator orautomatically by a control system) to avoid critical failures and topromote proper functioning, performance, and longevity of operatingengines. In this regard, a presence of wear metals in engine oil may bea significant concern.

Common factors that influence wear metal concentration in an oil sampleinclude: type of equipment, environment, the job it is performing,operator skill, length of time the oil has been in use, oil consumption,etc. Various laboratory methods for detection of abnormal levels of weardebris in used machine oils include: elemental analysis, ferrous densityanalysis, particle counting, and patch testing. For critical engines,testing for wear metals using a plurality of analytical techniques maybe employed, since limited wear metal testing using only one or twoconventional methods may fail to detect early-stage oil conditionsindicating impending engine failure.

The metal identity and concentration may be used to identify a varietyof faults. For example, detection of wear metals in engine oil mayindicate specific types of engine-wear and may be used to diagnose andrecommend preemptive action. For example, engine oil having elevatedaluminum may indicate that there is an issue with a piston. Increasediron in engine oil may indicate a problem with a cylinder liner, orincreased chromium in engine oil may indicate a problem with a cylinderring.

However, diagnosing a problem by the presence of metal in fluids such asengine oil, in particular, is complex. Copper detection illustrates thecomplexity of “diagnosing” a condition, because copper may be present inengine oil for a variety of reasons including: (i) abnormal wearsituation; (ii) contamination due to a coolant leak, which may beconsidered a problem; (iii) contamination due to “leaching” of copperfrom the oil-side of a cooling system, which may not be considered aproblem; and (iv) as an additive, that is, as an antioxidant in anoil-additive package.

Beyond metals, per se, there are other potential wear elements that maybe tested using laboratory-based spectrometric techniques. As in thecase of copper, a presence of silicon in engine oil may arise fromseveral sources. For example, a presence of silicon in engine oil mayindicate a possible coolant leak, leaching of silicone gaskets andsealants, or may be due to use of poly methyl siloxane additives inengine oil as an additive.

The following is a non-limiting example of the metals and non-metalsthat laboratory-based spectrometric techniques may identify in engineoil: aluminum, chromium, iron, copper, lead, tin, molybdenum, nickel,manganese, silver, lithium, titanium, potassium, boron, silicon, sulfur,sodium, calcium, magnesium, phosphorous, zinc, and barium.

An example set of predictive outcomes that may be used to diagnosesituations based on analytical evaluation of one or more target testdata inputs, for example, viscosity, soot, oxidation, fuel dilution, andwear metal identification. For example, dirt entry may be determined bythe presence of silicon (Si) and aluminum (Al), usually in the rangebetween about 2:1 to about 10:1.

Piston torching is a condition which originates from the use of siliconcarbide in the piston crown to reduce the coefficient of expansion.Determining piston torching using conventional oil analysis methodologyis rarely possible, as failure is usually rapid and there is littlechance of getting a sample while piston torching is occurring. However,using the disclosed methods and systems, piston torching may bedetermined by evaluating a ratio of silicon (Si) and aluminum (Al),which is typically a ratio of about 2:1.

Disclosed systems may also predict a presence of iron (Fe). Since ironis commonly used in the construction of engine components, high iron(Fe) content alone may indicate general wear or a presence of rust.

Disclosed systems may also predict a presence of elevated silicon (Si)quantity alone. Silicon by itself comes from a few mainsources—anti-foaming agents, additives, grease, and silicon sealants.Elevated silicon (Si) alone may indicate new/recently overhauledcomponents.

Top end engine wear may also be determined by determining a presence ofa combination of markers or targets. For example, top end engine wearmay be characterized by increased levels of Fe derived from a cylinderliner, elevated Al derived from wearing pistons, elevated chromium (Cr)derived from wearing engine rings, and elevated nickel (Ni) derived fromwearing camshaft.

Bottom end engine wear may be characterized by increased levels of Federived from a crankshaft, lead (Pb), copper (Cu), tin (Sn) derived fromwhite metal bearings and bronze bushings. Bottom end engine wear mayoften be precipitated by reduced base number (BN) or over-cooling asbearings become subject to corrosion from combustion byproducts (e.g.,acids). Fuel dilution may cause bottom end engine wear. Therefore,determining base number and fuel dilution in oil samples may be used tocharacterize engine wear overall.

When engines overheat oil may vaporize, but additive content does notvaporize. Extended engine overheating reduces the engine oil level andrequires addition of oil to the engine. Adding oil without performing anoil change has an additive effect of increasing the concentration ofadditives. Engine routinely overheat may be identified based on thepresence of increased additive levels, such as magnesium (Mg), calcium(Ca), zinc (Zn), phosphorous (P), and sulfur (S) as well as an increasein viscosity. Oxidation may be masked by adding additional oil that is,topping off the engine oil. Such topping off replenishes antioxidantsand boosts the BN. If engine overheating is prolonged, engine bearingsmay begin to wear resulting in increased lead (Pb), tin (Sn), and copper(Cu) may in engine oil.

Other wear conditions, which may be identified by oil conditionmonitoring, include bronze bushing wear and bronze gear/thrust washerwear. In engines where bronze bushing wear and/or bronze gear/thrustwasher wear occurs, oil conditions include increased copper (Cu) and tin(Sn) levels. Specifically the Cu: Sn ration is 20:1.

Internal coolant leaks may also be identified by monitoring oilconditions. For example, oil samples having increased sodium (Na), boron(B), copper (Cu), silicon (Si), aluminum (Al), and iron (Fe) may beobserved. While not all of these elements may be present they may alsobe accompanied by increased levels of lead (Pb), copper (Cu), and tin(Sn) as white-metal bearing wear often accompanies coolant leaks.

Roller bearing wear may also be detected by monitoring oil condition.For example, roller bearing wear may be identified by increased levelsof iron (Fe), chromium (Cr) and nickel (Ni). Increases in iron (Fe),chromium (Cr) and nickel (Ni) due to roller bearing wear may also beaccompanied by increases in copper (Cu) if brass/bronze cages areemployed in the engine/system configuration.

Hydraulic cylinder wear may also be detected by monitoring oilcondition. Increases in iron (Fe), chromium (Cr) and nickel (Ni) mayalso be indicative of hydraulic cylinder wear.

In some example instances, decreased oil viscosity may be the leadingindicator of critical issues associated with engine oil conditionmonitoring followed by soot and fuel dilution. In other exampleinstances, elevated copper levels may be the most common sign ofmoderate engine issues followed by soot and fuel dilution.

FIG. 60 is a graph of viscosity vs. fuel content in oil that may allowdetection of an anomalous fluid condition, according to an exampleembodiment of the present disclosure. As shown in FIG. 60, a presence offuel in engine oil leads to a decrease in viscosity. Reduction of engineoil viscosity may lead to increased engine wear. Thus, monitoring ofengine oil viscosity may be beneficial in avoiding engine wear.According to an embodiment, a viscosity threshold may be defined and ananomalous condition of the engine oil may be defined as a condition inwhich the measure viscosity drops below the predefined threshold.

Other anomalous engine oil conditions may also be identified bymonitoring spectroscopic data to detect a presence of variouscombinations of wear metals and/or contaminants (e.g., fuel, coolant,etc.). For example, specific frequency windows may be identified basedon a model of spectroscopic data for a particular material (e.g.,coolant model, fuel model, soot model, etc.), as described above. Suchspecific frequency windows may be monitored over time to detect changesin spectroscopic features. As with viscosity, described above,predetermined thresholds for the various frequency windows may bedefined and anomalous conditions may be detected when one or morepredefined thresholds are exceeded.

Various modifications may be made to the disclosed embodiments withoutdeparting from the scope or spirit of this disclosure. In addition or inthe alternative, other embodiments may be apparent from consideration ofthe specification and annexed drawings. Disclosed examples provided inthe specification and annexed drawings are illustrative and notlimiting. Although specific terms are employed herein, they are used ina generic and descriptive sense only and not for purposes of limitation.

What is claimed is:
 1. A processor implemented method of controlling aRaman sub-sampling system, the method comprising: performing, by aprocessor circuit, a plurality of spectroscopic measurements, eachspectroscopic measurement comprising: providing one of a plurality ofpower values to an excitation source that generates radiation with acorresponding intensity; receiving a signal from a detection systemrepresenting radiation scattered/emitted from a sample in response tothe sample having received radiation from the excitation source;determining a metric for the received signal; determining an optimumpower value corresponding to a value of the metric indicating a highestquality signal; and performing a Raman spectroscopy measurement on thesample using radiation generated by providing the optimum power value tothe excitation source, to thereby generate a Raman spectrum for thesample.
 2. The method of claim 1, further comprising: performing theplurality of spectroscopic measurements using increasing values of powerprovided to the excitation source starting from a baseline power level.3. The method of claim 2, further comprising: providing a baseline powervalue of 200 mW to the excitation source.
 4. The method of claim 1,further comprising: performing the plurality of spectroscopicmeasurements using increasing and decreasing values of power provided tothe excitation source.
 5. The method of claim 4, further comprising:performing the plurality of spectroscopic measurements using values ofpower provided to the excitation source that vary in increments between1 mW and 15 mW.
 6. The method of claim 1, further comprising: performingone of the plurality of spectroscopic measurements using a powerprovided to the excitation source that is chosen based on a power valueof a previous one of the plurality of spectroscopic measurements.
 7. Themethod of claim 1, further comprising: performing one of the pluralityof spectroscopic measurements using a power provided to the excitationsource that is chosen based on a metric of a previous one of theplurality of spectroscopic measurements.
 8. The method of claim 1,further comprising: determining the metric based on a processing modelthat characterizes the signal in terms of a presence of featuresindicative of Raman scattering and/or features indicative offluorescence.
 9. The method of claim 1, further comprising: performingthe plurality of spectroscopic measurements using values of powerprovided to the excitation source that include a predetermined number ofpredetermined power values.
 10. The method of claim 1, furthercomprising: performing the plurality of spectroscopic measurements usingvalues of power provided to the excitation source, wherein the number ofpower values is variable and depends on a convergence parameter relatedto the metric.